Reviewerly/exHarmony huggingface dataset:
Agent/Client Speech Identification for Mixed-Channel Conversation in Customer Service Call Centers

In our recent work, we proposed a method to use automatic speech recognition (ASR) to monitor customer service call centers. Specifically, text decoded by ASR is used as the input for a natural language understanding unit to achieve phone call categories, phone call scores or detect abnormal phone calls, etc. To do this, we need to have two separated audio streams from call centers i.e., one for agent, one for client side. However, in many call centers in Vietnam, the agent and client audio streams are mixed together due to many reasons such as hardware capacity, storage saving. In this paper, we propose a method to identify agent and client speech in a mixed-channel conversation. We first cluster speech segments in a mixedchannel conversation into two clusters using acoustic information. After that, language modelling technique works together with an ASR system to identify the agent/client label for each segment in the conversation. Experimental results show that the proposed method works effectively with over 95% of accuracy for both agent and client sides. Our approach has been applied successfully in several call centers in Vietnam.
EDUCATIONAL OR SCIENTIFIC CAREER?

One problem that arises when teaching in higher educational institutions is conducted by university professors is that the educator spends most of the time on their own scientific activities, to the detriment of the pedagogical orientation of the learning process and methodology.In addition, their requirements for research and publication activity are constantly increasing, and so we often encounter the concentration of teachers' attention on a scientific orientation.As a result, these teachers sometimes neglect the process of teaching students, which leads to a loss in the importance of the pedagogical component of the educational process.The overall nature of the problem lies in the importance of teaching in universities, which must be places where teaching is considered a priority activity, comparable to science and research.Therefore, in our environment, we can observe a gradually increasing offering of programs to strengthen the pedagogical competencies of teachers.In this article, we decided to focus on the pedagogical component of the educational process of teachers of higher educational institutions of the Chuvash Republic and their participation in the educational program.their pedagogical work, leaving it for some specialized scientific centers.Also, all respondents would like to pay more attention to the pedagogical process of teaching students -transferring knowledge, communicating with students or teaching students logical thinking, and forming high-quality professional competencies in their pupils.They also pointed out that attempting to save time in the learning process of students forms a "time bomb," which will further negatively affect the socio-cultural development of our society.Most of the respondents believe that the best way to develop their pedagogical competencies is to regularly communicate with their mentors, who are people who they deem as more professionally competent.They do not believe that the use of advanced training programs is of paramount importantance.
Mid-level Features for Categorization of Social Interactions in Public Spaces

The paper proposes mid-level features for socio-cognitive classification of crowd behavior in public spaces, particularly in the context of monitoring social interactions during, e.g., pandemic restrictions. The classification method follows a recently proposed categorization [37]. The features are built using statistics obtained from detection and tracking results forc crowd components, i.e. individuals and their groups (any typical detectors and trackers can be used). The features are defined by static (if obtained from the current frame) or dynamic (if derived from consecutive frames) parameters characterizing the crowd. Subsequently, the features extracted from a number of most recent frames are fed into a fully-connected shallow neural network to identify the type of social interactions in the monitored space. The experimental feasibility study shows encouraging performances of the approach. In particular, the results are far more discriminative than in the other solution (which, at the moment, is the only publicly known benchmark).
Performance Analysis of Commodity Server with Freeware Remote Terminal Application in Homogeneous and Heterogeneous Mutli-computing Environments

The technology is bringing new changes rapidly in terms of hardware and software which leads to the increase in the computing cost. One of the prevalent challenges involved in the research endeavors of researchers is to diminish the cost involved in possession of hardware and network technologies. Open source technologies have tried to cut down costs involve at software level, but learning on the software technologies is still a challenge. Commodity hardware utilization is still an option to decrease the hardware level cost. This paper presents the study of the performance of a commodity hardware server in heterogeneous multi-computing environment. Using the commodity hardware of our university and on a client–server-based model, we have developed a system of remote terminal application on a commodity server in homogeneous and heterogeneous multi-computing environments. The nodes used in the current experimental environment are independent of system architecture.
An APT Group Knowledge Model based on MDATA

Situational awareness is significant for cyber security, which can help researchers and security analysts obtain the network security situation comprehensively and accurately. Advanced Persistent Threat (APT) attack could cause severe consequences to cyberspace and detecting such attacks have become a very important part of cyber security situational awareness. Some APT attacks may belong to a same group, many countries and organizations have established databases for APT groups, such as adopting knowledge graph (KG) to represent the knowledge. However, cyberspace security knowledge varies by temporal and spatial characteristics, such as the attack technologies are updated very frequently, traditional KG cannot represent such knowledge timely. To address this problem, the MDATA (Multi-dimensional Data Association and inTelligent Analysis) model is proposed in [1], which is a supplement and improvement to traditional KG. In this paper, we introduce an APT group knowledge model based on MDATA, which adds spatial-temporal characteristics of the APT groups. We also analyze how this knowledge model could help address the challenges of APT attack awareness.
Implementing Authentication System Depending on Face Recognition Using Anthropometric Model

This paper exhibits an automatic method for detecting significant facial features point employing an advanced anthropometric face type. The facial features point the system work on are concerning the areas of mouth, nose, eyes and eyebrows. The anthropometric means the scientific study of the measurements and proportions of the human face. Several processes are performed in order to recognize human personality authenticated or not; these processes are beginning by capturing colored image using fixed digital camera and ending by features isolated into separated sub-images, and the lengths and distances among them representing authenticated person’s information are stored into database. In authentication stage, all the extracted features are compared with stored authenticated facial features in the database; the person is authenticated if a percentage of similarity equal to or greater than 83% is achieved.
ROAM-based visual tracking method

In this paper, we improved the ROAM tracking model as well as replaced the first 12 layers of VGG-16 used in the experiment with the HETCONV convolution layer, where the first layer remains unchanged, as well as the other layers are divided into 4 parts. Each part has only one 3*3 convolution kernel, as well as the other convolution kernels are all 1*1, thus improving the speed of the tracking model while keeping the accuracy unchanged. We have extensively evaluated the improved ROAM as well as the unimproved ROAM on the TrackingNet benchmark, as well as the results prove that this method effectively improves the frame rate.
Development and Deployment of an Autonomous UAV-Borne Gas and Particulate Sample Capture System for Fumarole Sampling

Volcanoes are one of the great forces of the natural world. The gases they release can reveal information about the world below us, from the structure of the planet, to the risk of an imminent eruption. Sampling these gases however is often difficult and extremely dangerous - high temperatures, hazardous gases, steep terrain and remoteness all make collecting samples a challenging endeavour. Unoccupied Aerial Vehicles (UAVs) can help reduce the risks and difficulties of measuring and sampling these gases, enabling studies of volcanic systems that were otherwise inaccessible. This paper presents the first known effort to design, develop and field test a UAV-borne Gas Capture System (UGCS) for volcanic fumarole sampling. This work includes the development of a sampling probe deployment mechanism, sample canister selection, payload-to-UAV interfacing, and a light-weight visual/thermal camera package. Operationally, the intricacies of placing a sample probe into a small fumarole opening are examined, as are the hazards of flying a UAV with a suspended load.
Design of a Robot for Healthcare Assistance in the Times of COVID-19

With the world reeling under the COVID-19 pandemic, maintaining distance from fellow humans is the new social interaction norm. During these unprecedented times, the medical fraternity and health workers are in dire need of additional help. Robots have been deployed for quite some time for facilitating industrial and healthcare needs. This paper tries to highlight the gap between affordable robots available for health institutions and the demand for them. The aim of this article is to design a prototype robot that can deliver food and medical trays to COVID-19 patients in hospitals to restrict human interaction and hence preventing the further spread of the disease. Autodesk Fusion 360, a computer-aided design tool, has been extensively used to design the robot.
Economic Assessment of Combination of Wind and Solar with Dual Axis Tracking and Anti-tracking for PV Panels

Decrease in capital costs, increase in life of operation, capacity factor and regularity in supply are the major factors that dictate the fixation of the levelized cost of energy for any power generation technology. The factor of intermittent supply is one of the main hurdles that prevents the proliferation of renewable forms of generation like solar and wind. The questionable figures associated capacity factor of the solar PV portfolio in certain regions also hamper its growth. This study conducts an overall economic assessment of combined solar/wind system, which is equipped with dual axis tracking/anti-tracking for PV panels. Such system promises to save the investment in capital costs. Subsequent evaluations of capacity factor have been carried out with the help of System Advisory Model (SAM) of NREL. The results are subsequently used to evaluate the overall decrease in levelized costs of energy after factoring in the change in capital costs associated with the combined system for a scale of 1 MW power generation. In conclusion the economic advantage of having solar tracking/ anti-tracking combined with wind generation at certain geographical locations is inferred.
Projectile Detection and Avoidance using Computer Vision

There are numerous projects based on bullet detection and calculating its trajectory. For example, using an infrared camera, the heat property of a projectile can be extracted to determine the position of the projectile at a given point in time. For these calculations, more serious equipment and preparations are required to achieve their efficiency. This paper presents a novel solution, which can determine from first-person view perspective screenshots, the trajectory of the approaching object and the movement necessary to avoid it, knowing the body parts of the endangered entity using image processing and optimization algorithms.
Construction of a Descriptive Framework for Grammatical Attributes of Relational Mark of Korean Compound Sentence Based on Knowledge Representation

The relational marks of Korean Compound Sentence (KCSRM) play an important role in compound sentence expression, so they have great research value in the field of language information processing. This paper systematically analyzes KCSRM with the help of relevant linguistic theories. Based on the knowledge representation theory of artificial intelligence (AI), this paper constructs a grammatical attribute description framework to formally describe KCSRM combined with framework representation. The research results can be directly used in the study of Korean complex sentence information processing.
Blockchain as an Imperative of Labor Relations Digitalizing

The article is devoted to the study of the prospects of using technology of blockchain, which allows you to consolidate information about transactions in a system of record chains distributed among users. Labor relations are one of the most widespread and regulated forms, which requires the provision of guarantees for entities. It is blockchain that can be the technology that will significantly change the world of work, save it from routine operations, formalization, losses, and costs that do not create value for the subjects. To identify the prospects of this technology a corresponding study was conducted. To understand the development prospects, we conducted a series of in-depth interviews with experts in the field of blockchain application, which allowed us to identify key obstacles, conditions for the success of implementation, the effect of the application in the field of labor relations. The study showed that at present under existing unstable conditions, subjects cannot clearly define the “rules” of relations and follow them for a long time. It is necessary to train all subjects of economic relations in the basics of the digital economy, IT technologies, and only after that proceed to the implementation of the blockchain in mass relations.
Estimating the moments of a random forcing field of 2D fluid from image sequences using energy minimisation method

Abstract In this paper, we consider a version of energy minimisation technique applied to images of a 2D fluid flow. The two Navier–Stokes equations describe the static flow of a 2D fluid in terms of velocity field, ( u , v ), pressure field, p and forcing field, f . Apart from these two Navier–Stokes equations, we have the incompressibility condition to evaluate the three parameters. While implementing this system, random noise (usually non-Gaussian) creeps into the random force field $$\underline{f}(x,y)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:munder> <mml:mi>f</mml:mi> <mml:mo>̲</mml:mo> </mml:munder> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:mi>y</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . We denote this random field by $$\delta \underline{f}(x,y)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>δ</mml:mi> <mml:munder> <mml:mi>f</mml:mi> <mml:mo>̲</mml:mo> </mml:munder> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:mi>y</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> having zero mean and non-trivial second and third moments. We assume that these two moments are known except for some unknown parameters $$\underline{\theta }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:munder> <mml:mi>θ</mml:mi> <mml:mo>̲</mml:mo> </mml:munder> </mml:math> (like mean, variance, co-variance, skewness, etc.) which we wish to estimate. In the proposed technique, we first calculate the approximate shift in the average fluid energy defined as a quadratic function of the velocity field. The energy method then requires that $$\underline{\theta }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:munder> <mml:mi>θ</mml:mi> <mml:mo>̲</mml:mo> </mml:munder> </mml:math> should be such that this average increases in the energy due to the random forcing component be minimised. We should, however, note that the standard statistical approach to force field estimation is to calculate the velocity field as a function of the force field and then adopt the statistical moment matching technique. Such an approach assumes spatial ergodicity of the velocity field. This approach to force field estimation is more accurate from the statistical moment matching view point but works only if velocity measurements are made. The former technique of energy minimisation does not require any velocity measurements. Both of these techniques are discussed in this paper and MATLAB simulations presented.
Virtual Vault: A Practical Leakage Resilient Scheme Using Space-Hard Ciphers

Due to the legal reform on the protection of personal information in US/Japan and the enforcement of the General Data Protection Regulation (GDPR) in Europe, service providers are obliged to more securely manage the sensitive data stored in their server. In order to protect this kind of data, they generally employ a cryptographic encryption scheme and secure key management schemes such as a Hardware Security Module (HSM) and Trusted Platform Module (TPM). In this paper, we take a different approach based on the space-hard cipher. The space-hard cipher has an interesting property called the space hardness. Space hardness guarantees sufficient security against the adversary who gains a part of key data, e.g., 1/4 of key data. Combined with a simple network monitoring technique, we develop a practical leakage resilient scheme Virtual Vault, which is secure against the snapshot adversary who has full access to the memory in the server for a short period. Importantly, Virtual Vault is deployable by only a low-price device for network monitoring, e.g. L2 switch, and software of space-hard ciphers and packet analyzer, while typical solutions require a dedicated hardware for secure key managements such as HSM and TPM. Thus, Virtual Vault is easily added on the existing servers which do not have such dedicated hardware.
Fast and Efficient Motion Planning using Directional Sampling-based Generalized Shape Expansion

Motion planning is a fundamental area of research in robotics and aerospace applications. Sampling-based methods provide an efficient solution to an otherwise challenging problem in motion planning. Recently, a sampling-based motion planner was presented based on the notion of novel 3D generalized shape to plan for an optimal collision-free path in 3D workspace and was termed as 3D-Generalized Shape Expansion (3D-GSE) algorithm. This was found to exhibit superior performance in terms of computational time and path costs over other well-established seminal algorithms in literature. Considering that a suitable directional sampling feature could potentially lead to a further superior performance of the 3D-GSE algorithm, this paper proposes two sampling schemes, namely basic and augmented directional sampling, and presents the 3D-GSE-D and 3D-GSE-AD algorithms, respectively. These algorithms, by default, have the benefits of the 3D-GSE algorithm. In addition, both the basic and augmented directional schemes sample random points with more preference towards the direction of the Goal point leading to lower path cost on average. While the basic directional scheme suffers from a higher computational time when the obstacle density is high along the direction towards the Goal, the augmented directional scheme is free from this drawback. Probabilistic analysis and extensive numerical simulation studies justify the performance of the 3D-GSE-D and the superiority of the 3D-GSE-AD in performance in terms of computational time efficiency and shortest path cost when compared with the 3D-GSE, existing directional and other seminal algorithms.
Development of Powered Wheelchair Expanding Range of Independent Activities of Physically-handicapped Person

In this research, we aim to develop a powered wheelchair expanding the range of independent activities for a physically-handicapped person. For this purpose, in this paper, we discuss the specifications of such a wheelchair. Its power train and drive unit are designed to satisfy the specifications. Furthermore, the powered mechanism controlling seat position and attitude is also investigated. As a result, a prototype miniature model has been produced to verify the availability of the proposed wheelchair system.
Predicting the Authenticity of Banknotes Using Supervised Learning

This research paper deals with using supervised machine learning algorithms to detect authenticity of bank notes. In this research we were successful in achieving very high accuracy (of the order of 99%) by applying some data preprocessing tricks and then running the processed data on supervised learning algorithms like SVM, Decision Trees, Logistic Regression, KNN. We then proceed to analyze the misclassified points. We examine the confusion matrix to find out which algorithms had more number of false positives and which algorithm had more number of False negatives. This research paper deals with using supervised machine learning algorithms to detect authenticity of bank notes. In this research we were successful in achieving very high accuracy (of the order of 99%) by applying some data preprocessing tricks and then running the processed data on supervised learning algorithms like SVM, Decision Trees, Logistic Regression, KNN. We then proceed to analyze the misclassified points. We examine the confusion matrix to find out which algorithms had more number of false positives and which algorithm had more number of False negatives.
Single Image Rain Removal Using Convolutional Neural Network

The visibility of the image is highly affected by the rain streaks. This can influence the performance of numerous visual tasks, for example, image enhancement, object tracking, recognition, surveillance and autonomous navigation. Process of recognition and removal of rain streaks is a quite complex and difficult task since there is no spatial-temporal content of rain streaks in a single image as compared to the dynamic video. This paper proposes an improved convolutional neural network (CNN) architecture to recognize and remove the rain streaks. Linear additive composite model is used for making rainy image model. Network is trained on the pre-processed image, which helps to enhance the learning of the network weights and training without huge increase in training data or computational resources. The experimental work shows that the CNN architecture successfully performed on both synthesized and real-world rainy images.