Reviewerly/exHarmony huggingface dataset:
Discretization Inspired Defence Algorithm Against Adversarial Attacks on Tabular Data

Deep learning methods are usually trained via a gradient-descent based procedure, which can be efficient as it is not only end-to-end but also suitable for large quantities of data. However, gradient-based learning is vulnerable to adversarial attacks – which account for unperceivable changes in the input data to misguide a trained model. Though a plethora of work explored the adversarial learning (attacks and defences) in image datasets, the exploration of adversarial learning in tabular datasets has seen little attention. In this work, we study adversarial learning in tabular datasets. We investigate the role of discretization and demonstrate that discretizing numeric attributes offers a strong defence mechanism. The main contribution of this work is the proposition of two new defence algorithms for numeric tabular datasets, that utilize cut-points obtained from discretization, to forge a defence against various forms of adversarial attacks. We evaluate the effectiveness of our proposed method on a wide range of machine learning datasets and demonstrate that the proposed algorithms lead to a state-of-the-art defence strategy on tabular datasets.
HST-GAN: Historical Style Transfer GAN for Generating Historical Text Images

This paper presents Historical Style Transfer Generative Adversarial Networks (HST-GAN) for generating historical text images. Our model consists of three blocks: Encoder, Generator, and Discriminator. The Encoder encodes the style to be generated, and the Generator applies an encoded style, $$\mathcal {S}$$ to an input text image, $$\mathcal {I}$$ , and generates a new image with the content of $$\mathcal {I}$$ and the style $$\mathcal {S}$$ . The Discriminator encourages the Generator to enhance the quality of the generated images. Multiple loss functions are applied to ensure the generation of quality images. We evaluated our model against three challenging historical handwritten datasets of two different languages. In addition, we compare the performance of HST-GAN with the state of art approaches and show that HST-GAN provides the best generated images for the three tested datasets. We demonstrate the capability of HST-GAN to transfer multiple styles across domains by taking the style from one dataset and the content from another dataset and generate the content according to the desired style. We test the quality of the style domains transferring using a designated classifier and a human evaluation and show that the generated images are very similar to the original style.
An Impact of Data Augmentation Techniques on the Robustness of CNNs

Data augmentation is a well-known and willingly used method of Convolutional Neural Network regularization. However, there are no clear recommendations on which augmentation strategy should be used. We performed the comprehensive analysis based on the CIFARs datasets and two models: the ResNet and the MobileNet. We trained models with a total of 21 different augmentation approaches. The analysis was based on non-modified and strongly distorted images in 9 different ways. It allowed us to focus on checking the robustness of the networks based on various scenarios. We’ve checked the influence of data augmentation on robustness. Finally, we suggested more general guidelines for improving accuracy and overall robustness increase.
Real-Time Detection of Face Mask and Social Distancing

According to the World Health Organization data, the global epidemic of COVID-19 has profoundly affected the world and has now infected more than eight million people worldwide. The COVID-19 epidemic forced governments worldwide to close their doors to prevent the spread of the virus. Reports indicate that wearing a facemask and maintaining public distance while at work reduces the risk of infection. CCTV cameras are installed in educational and industrial spaces, residential areas, and crowded places. However, it is difficult to physically monitor buildings to find people without masks and people who do not keep public distance, and it is challenging to monitor video footage of large buildings and find people without a facemask and social distance correction. A mixed model that uses in-depth reading and machine learning to face masks and correct public distances will help managers look after people roaming the grounds without hiding their faces and distance from the public. The supervised machine-learning algorithm will train the model with the data available online. The facemask data acquisition set contains masked and non-masked images. OpenCV will be used for real-time face recognition and social distance adjustment from live streaming videos from web-enabled videos. The Convolution Neural Network will be used to extract features from the database. After the feature removal process, the newly installed photo/video will be classified as face masked or face masked. The proposed methodology has shown that person detection, social distance, and F.I. scores approximate 91.2%, and the average F1 score is 90.79%.
CrossAligner &amp; Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding

Task-oriented personal assistants enable people to interact with a host of devices and services using natural language.One of the challenges of making neural dialogue systems available to more users is the lack of training data for all but a few languages.Zero-shot methods try to solve this issue by acquiring task knowledge in a high-resource language such as English with the aim of transferring it to the lowresource language(s).To this end, we introduce CrossAligner, the principal method of a variety of effective approaches for zero-shot cross-lingual transfer based on learning alignment from unlabelled parallel data.We present a quantitative analysis of individual methods as well as their weighted combinations, several of which exceed state-of-the-art (SOTA) scores as evaluated across nine languages, fifteen test sets and three benchmark multilingual datasets.A detailed qualitative error analysis of the best methods shows that our fine-tuned language models can zero-shot transfer the task knowledge better than anticipated.
Scrutinization of Urdu Handwritten Text Recognition with Machine Learning Approach

Urdu Nastaliq script is a cursive and ligature-based script that is very difficult to recognize and detect using current computer algorithms dues to its complicated writing structure . Handwritten text imposes much more difficulty since all writers have their own writing styles. Many researchers have worked in this field on various aspects, from creating datasets to improving the recognition rate for its text. This paper is a literature survey on the work done for Urdu handwritten text recognition by optical character recognition using deep learning and machine learning techniques. It also compares the accuracy rates of respective algorithms over different datasets.
Deep Reinforcement Learning in Agents’ Training: Unity ML-Agents

Video games are an area where Artificial Intelligence has multiple application scenarios, allowing to add improvements that can be applied to provide greater realism in the game experience, accelerate its development (even automate it) and save costs, among other benefits. Beyond the commercial vision and from a research point of view, different strategies and algorithms are applied in certain facets/applications that pose a significant challenge in terms of the development of these algorithms and their applicability (in this area and others). These applications include the creation of intelligent agents (which can be cooperate or adversarial), the automatic generation of content (structures, characters, scenarios, etc.), the modeling of player behavior and habits, and particular rendering techniques. This paper focuses on the use of the open source project Unity ML-Agents Toolkit to train different intelligent agents using Deep Reinforcement Learning techniques and associated learning algorithms applied to this scenario of Artificial Intelligence use.
Estimating Frontal Body Landmarks from Thermal Sensors Using Residual Neural Networks

In this work, we propose the use of thermal vision sensors to estimate the frontal body landmarks of an inhabitant. The use of thermal sensors is being promoted to collect human patterns while protecting inhabitants’ privacy in smart environments. On the other hand, deep learning approaches have provided encouraging results in estimating body, hand and facial landmarks. Here, we present a residual neural network which produces body landmarks from images collected by a low cost thermal sensor. In order to solve the problems of capturing and labeling data, which hinder learning in deep learning models, we propose an auto-labeling approach with dual visible-spectrum and thermal cameras, including the recognition of keypoints by the OpenPose model. A case study developed with four inhabitants in different poses shows encouraging results.
Authentication in the Bounded Storage Model

We consider the streaming variant of the Bounded Storage Model (BSM), where the honest parties can stream large amounts of data to each other, while only maintaining a small memory of size n. The adversary also operates as a streaming algorithm, but has a much larger memory size $$m \gg n$$ . The goal is to construct unconditionally secure cryptographic schemes in the BSM, and prior works did so for symmetric-key encryption, key agreement, oblivious transfer and multiparty computation. In this work, we construct message authentication and signatures in the BSM. First, we consider the symmetric-key setting, where Alice and Bob share a small secret key. Alice can authenticate arbitrarily many messages to Bob by streaming long authentication tags of size $$k \gg m$$ , while ensuring that the tags can be generated and verified using only n bits of memory. We show a solution using local extractors (Vadhan; JoC ’04), which allows for up to exponentially large adversarial memory $$m = 2^{O(n)}$$ , and has tags of size $$k= O(m)$$ . Second, we consider the same setting as above, but now additionally require each individual tag to be small, of size $$k \le n$$ . We show a solution is still possible when the adversary’s memory is $$m = O(n^2)$$ , which is optimal. Our solution relies on a space lower bound for leaning parities (Raz; FOCS ’16). Third, we consider the public-key signature setting. A signer Alice initially streams a long verification key over an authentic channel, while only keeping a short signing key in her memory. A verifier Bob receives the streamed verification key and generates a short verification digest that he keeps in his memory. Later, Alice can sign arbitrarily many messages using her signing key by streaming large signatures to Bob, who can verify them using his verification digest. We show a solution for $$m= O(n^2)$$ , which we show to be optimal. Our solution relies on a novel entropy lemma, of independent interest. We show that, if a sequence of blocks has sufficiently high min-entropy, then a large fraction of individual blocks must have high min-entropy. Naive versions of this lemma are false, but we show how to patch it to make it hold.
COMMUNICATION TASKS OF HIGHER EDUCATION INSTITUTIONS IN THE TRANSFORMATION OF THE MODERN MEDIA FIELD

The article deals with the emergence of new technologies for managing the reputation of the university in the digital information environment due to the transformation of the modern media space and the changing functions of the media. In modern conditions the universities are in a difficult situation: on the one hand, the competition and desire to attract applicants force them to intensify marketing efforts, confirming the positioning of higher education as a service, a special kind of service. On the other hand, such positioning weakens the role of higher education institution in the development of human capital and deprives it of its special intellectual mission. The integral profile of the university in the media environment and, first of all, in its digital segment, is formed through a set of different channels, through which the meanings and key attitudes about the character of the organization are transmitted. The peculiarity of the university’s communication activities lies in the hyper-fragmentation of its channels. Additional risks require controlling the media field and allocating highly professional human and financial resources, as well as increasing the competence of specialists in their own structures that influence the formation of the information environment of the university.
One Agent To Rule Them All: Towards Multi-agent Conversational AI

The increasing volume of commercially available conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks.Though prior work has explored supporting a multitude of domains within the design of a single agent, the interaction experience suffers due to the large action space of desired capabilities.To address these problems, we introduce a new task BBAI: Black-Box Agent Integration, focusing on combining the capabilities of multiple black-box CAs at scale.We explore two techniques: question agent pairing and question response pairing aimed at resolving this task.Leveraging these techniques, we design One For All (OFA), a scalable system that provides a unified interface to interact with multiple CAs.Additionally, we introduce MARS: Multi Agent Response Selection, a new encoder model for question response pairing that jointly encodes user question and agent response pairs.We demonstrate that OFA is able to automatically and accurately integrate an ensemble of commercially available CAs spanning disparate domains.Specifically, using the MARS encoder we achieve the highest accuracy on our BBAI task, outperforming strong baselines.
Multi-granularity Evolution Network for Dynamic Link Prediction

Dynamic link prediction target to predict future new links in a dynamic network, is widely used in social networks, knowledge graphs, etc. Some existing dynamic methods capture structural characteristics and learn the evolution process from the entire graph, which pays no attention to the association between subgraphs and ignores that graphs under different granularity have different evolve patterns. Although some static methods use multi-granularity subgraphs, they can hardly be applied to dynamic graphs. We propose a novel Temporal K-truss based Recurrent Graph Convolutional Network (TKRGCN) for dynamic link prediction, which learns graph embedding from different granularity subgraphs. Specifically, we employ k-truss decomposition to extract multi-granularity subgraphs which preserve both local and global structure information. Then we design a RNN framework to learn spatio-temporal graph embedding under different granularities. Extensive experiments demonstrate the effectiveness of our proposed TKRGCN and its superiority over some state-of-the-art dynamic link prediction algorithms.
The State of the Art Text Summarization Techniques

With the advent of communication technology, a tremendous amount of data is generated. The availability of a vast amount of data provides information and presents the challenge of extracting knowledge from it. The solution to such an issue is text summarization. The documents are examined, and a thorough, compact, and relevant summary is generated using in-text summarization. It is classified into two forms based on the approach used: extractive and abstractive summarization. Extractive summarization selects words and sentences from an existing document to create a summary. Semantic analysis is performed in the case of Abstractive summarization, and new words and phrases are employed to construct the summary. We’ve gone over the many types of text summarization in detail in this paper, along with a discussion of the various research approaches that have been used so far.
The relation of metacognitive involvement with personal intellectual assets and the development of universal competences of students

The paper discusses why having preserved and sometimes high-level intelligence, a person is not always successful in educational activities. According to the studies, planning and monitoring skills and the skill to control the process of own activity (metacognitive involvement) can determine success in learning. This work aims to study the relations between metacognitive involvement and the personal intellectual resources of students. The authors based the research methodology on structural equation modeling (SEM). The study involved 140 respondents – senior class students of schools and universities. The study showed the existence of positive relations between metacognitive involvement, universal competencies, and personal intellectual resources. The authors identified that women had higher cooperation indicators than men; the students of humanitarian training programs had higher communication indicators; academic performance indicators were low-positively associated with metacognitive involvement. The authors conclude that metacognitive processes and qualities are associated with the general abilities of a person, the structure of which involves metacognitive components. As a result of factor analysis, the authors developed a two-factor model: Metacommunication (F1 factor), which indicates individual intellectual resources and universal competencies, manifested in activity and communication, and Cognitive abilities (F2 factor), manifested in thinking. The study identified the relations between the academic success indicators, factors of metacognitive involvement, and metacommunications of students.
Motion Sensors Based Human Arm Pose Estimation

The elements of a human arm, forearm to upper arm, execute a series of motions to perform certain actions during daily activities. The performed actions are the result of combining the flexion-extension, abduction-adduction in different orders and with different angles. To capture the motions and use them for different applications, the angles for the arm's elements are needed for all three planes, coronal, transversal and sagital, during the motion. The proposed prototype has three MPU6050 IMU sensors connected to an Arduino Uno development board, through a TCA9548A demultiplexing chip, which transmits the captured motion data to a computer that uses MATLAB to post-process the data and simulate the motion in a 3D environment. The system has a reference situated on the spine and two points along the human arm, to capture its motion, the first in the middle of the Humerus bone, and the second is in the middle of the forearm. We've proposed this prototype, not only for accuracy, cost-effective hardware-wise, but also, operating it, represents and advantage, allowing flexibility and ease of use. This prototype has the capability not only to capture the human arm's motion, in real time, but also to discover the natural limits of the motions which a human arm can do. The sets of motion, that follow daily routines for motion, of a person, can be integrated in an exoskeleton's control system, to reproduce scenarios with the help of which possible patients can train themselves, or use in therapy, in order to regain mobility of their arms, that were formerly affected by neuromuscular diseases or physical injuries of the muscles or bones.
Distance-GCN for Action Recognition

Many skeleton-based action recognition models have been introduced with the application of graph convolutional networks (GCNs). Most of the models suggested new ways to aggregate adjacent joints information. In this paper, we propose a novel way to define the adjacency matrix from the geometrical distance between joints. By combining this method with the formerly used adjacency matrix, we can increase the performances of graph convolution layers with slightly increased computational complexity. Experiments on two large-scale datasets, NTU-60 and Skeletics-152, demonstrate that our model provides competitive performance.
Ensure the Confidentiality of Documents Shared Within the Enterprise in the Cloud by Using a Cryptographic Delivery Method

Cloud-based software services have recently gained popularity due to their architecture, which provides numerous benefits to their users. These services, for ex-ample, enable remote users to work transparently on legacy software provided by an enterprise, with all the resources required. Consider office documents or databases that are used by internally developed software. In this paper, we are interested in the specific case where multiple users must work concurrently on shared office documents while reaping the benefits of the cloud without compromising the confidentiality of the manipulated data. We propose a process to protect the confidentiality of data passing through the provider. This technique allows us to use cloud services without relying on the cloud’s confidentiality of our data. Our method also enables us to digitally sign the changes made to the document, preventing a malicious cloud from altering the data. The cloud can alter the data. The presented technique is also compatible with using a secure element that encapsulates cryptographic operations in hardware. At the hardware level, operations are carried out. The proposed process applies to content that does not necessitate interpretation by the service provider.
Real-Time Translation of Indian Sign Language to Assist the Hearing and Speech Impaired

The most predominant mode of communication among people with hearing and speech impairment is the sign language. Therefore, if those concerned individuals are not equally skilled in sign language, there will be a communication barrier between them, thereby making the availability of an interpreter/translator immensely important for communication. Efficient recognition of gesture-based communication, hence, becomes essential to overcome the obstacle to communication among the people with hearing/speech impairment and people without such impediment. The ISL gesture motions are recorded by utilizing a mobile camera, and these pictures are then processed appropriately by our model. The user receives the output which is returned by the SoftMax layer as the most probable class after comparing confidence scores. The model created as part of this project availed us an accuracy of 91.84% under the adaptive thresholding filter. An accuracy of 92.78% was determined under the hybridized SIFT mode with adaptive thresholding filter.
6D Pose Estimation of Transparent Objects Using Synthetic Data

Transparent objects are one of the most common objects in everyday life. The poses of these objects must be estimated to pick and manipulate such objects. However, recognizing and estimating the poses of transparent objects is still a challenging task in robot vision, even after the emergence of 3D sensors. It is difficult to detect transparent objects because of the absorption and refraction of light, and the appearance of transparent objects can vary in different backgrounds. In this work, we propose a simple yet effective method for transparent object pose estimation, in which we address the problem using a synthetic dataset to train a deep neural network and estimate the pose of known transparent objects. After creating a synthetic dataset for transparent objects, we used a one-shot deep neural network to estimate the 6D pose of a known object. To the best of our knowledge, this is the first time synthetic data have been used for transparent object pose estimation. We conducted experiments on 3D printed transparent objects in a real environment as well as in a simulation environment. The results show that the proposed method can successfully estimate the pose of a transparent object even though it is only trained using synthetic data.