Reviewerly/exHarmony huggingface dataset:
Some Aspects of Automated Implementation of Dependencies in PHP Language

The purpose of the article is to research, analyze and consider the general problems and prospects of using the principle of implementing dependencies in the development of web applications in the PHP language. Research methods are methods of semantic analysis of the basic concepts of this subject area (web technologies and web applications). The article considers the existing approaches to control inversion based on the use of the dependency implementation template. The novelty of the research is to solve the control inversion problems based on a template when designing web applications. The conclusion of the research conducted in the article is that the existing problems and prospects for the application of the principle of dependency implementation in web applications with program code written in PHP have been analyzed. Taking into account the results of the analysis, the authors have developed a system for automating the implementation of dependencies, which is important for solving problems of improving the efficiency of web application development processes in PHP.
Writing Java Programs

In this chapter, you will learn: the structure of a Java program, how to write a Java program, what a module graph is, and how to use the classpath and modulepath.
Using Python scripting to enhance workflow in Autodesk Maya

Coding empowers automation. Scripts can handle mundane and repetitive tasks in an efficient and precise manner. This course will offer will use an hands-on interactive format to walk attendees through representative scripting projects, selected to be useful for everyday workflows. It is intended to be an intermediate course. The goal is to cover provide enough information for attendees to build on later. Python scripting can automate many tasks in Maya, from running simple commands to developing plug-ins. Attendees will learn how to automate simple tasks using the magic of scripting, through four distinct projects. The course will placing objects randomly in a scene, designing custom User Interfaces (GUIs) in Maya, scripting MASH (motion graphic) networks, and scripting a leg rig, with foot-roll. By the end of the course, attendees should walk away with a solid understanding of the power Python scripting and Maya commands provide, and the the ability to build their own advance projects for Maya. This course will equip attendees with the tools, confidence, and initiative to explore more advanced scripts independently. Attendees should have programming experience, preferably in Python, but a solid grasp of the foundational programming constructs should suffice. Attendees should have Autodesk Maya, Python, and Visual Studio Code pre-loaded on their devices if they intend to follow along.
The Next Step in DBMS Management

One reason why companies adopted database management systems (DBMS) is these systems helped to integrate the autonomous nature of corporate information. Just as the Web dramatically altered the way companies build daily business applications; it is changing the way application, network equipment, DBMS, and systems vendors design their management applications. Rather than continue to be locked into proprietary APIs that hinder interoperability and increase development costs, suppliers are moving to open, Web-based systems. After taking on Web Based Enterprise Management development, the Desktop Management Task Force set out to develop the Common Information Model (CIM), an object-oriented information model that provides a conceptual framework within which any management data may be modeled. Extensible Markup Language provides a way of identifying structured management information exchanges so applications can trade CIM data.
STUDY OF PSYCHOLOGY BY STUDENTS AT THE AGRICULTURAL UNIVERSITY

The article is devoted to the issue of application and use of active teaching methods in the study of psychology by students of agricultural universities. It is shown that the full potential of these methods can be revealed during practical classes and organized independent work of students. The object of the study may be the members of the student group, and methods should be chosen so that on the one hand, they can be implemented in a group study for a limited time, and on the other - the studied phenomena correspond to the theoretical program of psychology. The article discusses the problems of technology of research and interpretation of results, as well as the possibility of using psychological achievements in professional and educational activities.
Software Platforms Based on the Principles of Graphic Design, Automatic Command Generation and Visual Programming

This paper presents a new approach to software application development using a graphical interface. The approach is based on a combination of drag and drop elements and logic based on the model’s own concept. Low code platforms and principles have been developed and are still being developed precisely to enable the rapid creation and use of applications that meet all the special needs and requirements of various organizations. No code platforms allow professionals and laymen to create applications via graphical user interfaces without any prior knowledge or qualifications in programming. However, code platforms are closely related to low code platforms because they are both created with a similar goal, based on a very similar way of working and almost the same principles of operation. Many vendors point out that the future of software development is based on configuration, not program. We believe that eliminating code is one way to bring development to higher standards in application development. One of the biggest advantages of the LC/NC platform is that they allow us to take advantage of innate problem solving and human skills by removing at least a significant number, if not all barriers to implementing software solutions in today’s software world.
Features of the distribution of molecular hydrogen in groundwater in various geological and tectonic conditions of Uzbekistan

Abstract As a result of generalization and analysis of previously performed work on the determination of hydrogen on the day surface, a picture of the distribution of hydrogen concentration in the aeration zone was obtained. The increased concentration of hydrogen on the day surface and in the aeration, zone is associated with the most favourable conditions for the transit of fluids from the folded Palaeozoic basement to the surface cover (upper part of the Earth’s crust). These zones are zones of active fluid conductivity, which leads to a high concentration of hydrogen on the day surface. Intense linear anomalies are confined to the zone of intersection of the Tuzkoy-Rometan transverse fault with the Bukhara-Gissar regional fault, with which seismic events are associated (epicentres Gazli earthquakes occurred 08.04.1976, 17.05.1976 and 19.03.1984). Analysis of long-term observations of gas composition on an example of molecular hydrogen in groundwater southwestern part Karzhantaus fault, covering the territory of Tashkent city and its surroundings, obtained hydrogen concentration distribution pattern area. A different concentration of hydrogen in the fault zone was revealed, associated with the heterogeneity of the geological and structural structure.
DESIGN OF AN EASY-TO-USE MOBILE AUGMENTED REALITY LEARNING SETTING BY MEANS OF A CONJECTURE MAP

Despite the positive effects of mobile augmented reality (MAR)-tools for learning, MAR-tools are not commonly used in classrooms.The scientific discourse identified a lack of concepts that guide the practical application of mobile augmented reality (MAR)-tools in education.Teachers often feel insecure when designing and applying digital learning settings and therefore need tools and concepts to support them.The present contribution outlines how to develop an easy-to-use mobile augmented reality learning setting (MARLS).Thus, a MARLS to foster artificial intelligence (AI)-literacy is developed and studied to explore what makes MARLS easy-to-use.AI-literacy serves as an exemplary topic.It appears to be suiting because MAR-technology is based on AI-systems and hence allows students to experience a positive form of human-AI-interaction first headedly.The educational design research is conducted by means of a conjecture map to enable the concurrent investigation of learning, teaching and its interdependence.Derived from the high-level conjectures, (I): AI-literacy is a set of competences, (II): a digital learning environment is crucial to foster it, (III): a good MARLS considers usability, user centeredness, conscious application, basic learning theories and cognitive load, the MARLS is developed.The MARLS and its conjecture map lay ground for the yet to follow measure and improvement of the learning design.
Unified Field Theory for Deep and Recurrent Neural Networks

Understanding capabilities and limitations of different network architectures is of fundamental importance to machine learning. Bayesian inference on Gaussian processes has proven to be a viable approach for studying recurrent and deep networks in the limit of infinite layer width, $n\to\infty$. Here we present a unified and systematic derivation of the mean-field theory for both architectures that starts from first principles by employing established methods from statistical physics of disordered systems. The theory elucidates that while the mean-field equations are different with regard to their temporal structure, they yet yield identical Gaussian kernels when readouts are taken at a single time point or layer, respectively. Bayesian inference applied to classification then predicts identical performance and capabilities for the two architectures. Numerically, we find that convergence towards the mean-field theory is typically slower for recurrent networks than for deep networks and the convergence speed depends non-trivially on the parameters of the weight prior as well as the depth or number of time steps, respectively. Our method exposes that Gaussian processes are but the lowest order of a systematic expansion in $1/n$. The formalism thus paves the way to investigate the fundamental differences between recurrent and deep architectures at finite widths $n$.
Blackbox Untargeted Adversarial Testing of Automatic Speech Recognition  Systems

Automatic speech recognition (ASR) systems are prevalent, particularly in applications for voice navigation and voice control of domestic appliances. The computational core of ASRs are deep neural networks (DNNs) that have been shown to be susceptible to adversarial perturbations; easily misused by attackers to generate malicious outputs. To help test the correctness of ASRS, we propose techniques that automatically generate blackbox (agnostic to the DNN), untargeted adversarial attacks that are portable across ASRs. Much of the existing work on adversarial ASR testing focuses on targeted attacks, i.e generating audio samples given an output text. Targeted techniques are not portable, customised to the structure of DNNs (whitebox) within a specific ASR. In contrast, our method attacks the signal processing stage of the ASR pipeline that is shared across most ASRs. Additionally, we ensure the generated adversarial audio samples have no human audible difference by manipulating the acoustic signal using a psychoacoustic model that maintains the signal below the thresholds of human perception. We evaluate portability and effectiveness of our techniques using three popular ASRs and three input audio datasets using the metrics - WER of output text, Similarity to original audio and attack Success Rate on different ASRs. We found our testing techniques were portable across ASRs, with the adversarial audio samples producing high Success Rates, WERs and Similarities to the original audio.
Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset

Automatic detection of weapons is significant for improving security and well being of individuals, nonetheless, it is a difficult task due to large variety of size, shape and appearance of weapons. View point variations and occlusion also are reasons which makes this task more difficult. Further, the current object detection algorithms process rectangular areas, however a slender and long rifle may really cover just a little portion of area and the rest may contain unessential details. To overcome these problem, we propose a CNN architecture for Orientation Aware Weapons Detection, which provides oriented bounding box with improved weapons detection performance. The proposed model provides orientation not only using angle as classification problem by dividing angle into eight classes but also angle as regression problem. For training our model for weapon detection a new dataset comprising of total 6400 weapons images is gathered from the web and then manually annotated with position oriented bounding boxes. Our dataset provides not only oriented bounding box as ground truth but also horizontal bounding box. We also provide our dataset in multiple formats of modern object detectors for further research in this area. The proposed model is evaluated on this dataset, and the comparative analysis with off-the shelf object detectors yields superior performance of proposed model, measured with standard evaluation strategies. The dataset and the model implementation are made publicly available at this link: https://bit.ly/2TyZICF.
Attacking Point Cloud Segmentation with Color-only Perturbation

Recent research efforts on 3D point-cloud semantic segmentation have achieved outstanding performance by adopting deep CNN (convolutional neural networks) and GCN (graph convolutional networks). However, the robustness of these complex models has not been systematically analyzed. Given that semantic segmentation has been applied in many safety-critical applications (e.g., autonomous driving, geological sensing), it is important to fill this knowledge gap, in particular, how these models are affected under adversarial samples. While adversarial attacks against point cloud have been studied, we found all of them were targeting single-object recognition, and the perturbation is done on the point coordinates. We argue that the coordinate-based perturbation is unlikely to realize under the physical-world constraints. Hence, we propose a new color-only perturbation method named COLPER, and tailor it to semantic segmentation. By evaluating COLPER on an indoor dataset (S3DIS) and an outdoor dataset (Semantic3D) against three point cloud segmentation models (PointNet++, DeepGCNs, and RandLA-Net), we found color-only perturbation is sufficient to significantly drop the segmentation accuracy and aIoU, under both targeted and non-targeted attack settings.
Competition between lasing and superradiance under spintronic pumping

We consider a platform featuring the minimal ingredients relevant both to a driven-dissipative cavity quantum optics system and to a spintronically pumped magnon condensate. We extend the Dicke model for a bosonic mode collectively coupled to a coherent spin ensemble, by weakly coupling it to an out-of-equilibrium spin bath which is tunable by incoherent pumping. Our model exhibits competition between coherent couplings and dissipative channels, which leads to various collective quantum behaviors, static and dynamic, depending on the interplay of spin-boson interactions and spin pump/loss rates. Our analysis exposes a hybrid lasing-superradiant regime which does not take place in an ordinary pumped Dicke spin ensemble. We interpret the resultant non-equilibrium phase diagram from both a quantum optics and a spintronics standpoint, supplying a conceptual bridge between the two fields. The implications of our results concern dynamical control in spintronics and frequency-dependent gain media in quantum optics.
TransCMD: Cross-Modal Decoder Equipped with Transformer for RGB-D  Salient Object Detection

Most of the existing RGB-D salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration. The inherent local connectivity of convolution operation constrains the performance of the convolution-based methods to a ceiling. In this work, we rethink this task from the perspective of global information alignment and transformation. Specifically, the proposed method (TransCMD) cascades several cross-modal integration units to construct a top-down transformer-based information propagation path (TIPP). TransCMD treats the multi-scale and multi-modal feature integration as a sequence-to-sequence context propagation and update process built on the transformer. Besides, considering the quadratic complexity w.r.t. the number of input tokens, we design a patch-wise token re-embedding strategy (PTRE) with acceptable computational cost. Experimental results on seven RGB-D SOD benchmark datasets demonstrate that a simple two-stream encoder-decoder framework can surpass the state-of-the-art purely CNN-based methods when it is equipped with the TIPP.
Leaping through Time with Gradient-Based Adaptation for Recommendation

Modern recommender systems are required to adapt to the change in user preferences and item popularity. Such a problem is known as the temporal dynamics problem, and it is one of the main challenges in recommender system modeling. Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies. LeapRec characterizes temporal dynamics by two complement components named global time leap (GTL) and ordered time leap (OTL). By design, GTL learns long-term patterns by finding the shortest learning path across unordered temporal data. Cooperatively, OTL learns short-term patterns by considering the sequential nature of the temporal data. Our experimental results show that LeapRec consistently outperforms the state-of-the-art methods on several datasets and recommendation metrics. Furthermore, we provide an empirical study of the interaction between GTL and OTL, showing the effects of long- and short-term modeling.
Unsupervised Federated Learning

In this chapter, we consider unsupervised learning tasks being implemented within the federated learning framework to satisfy stringent requirements for low-latency and privacy of the emerging applications. The discussed algorithm is based on Dual Averaging (DA), where the gradients of each agent are aggregated at a central node. While having its advantages in terms of distributed computation, the accuracy of federated learning training reduces significantly when the data is nonuniformly distributed across devices. Therefore, this chapter discusses two weight computation algorithms, with one using a fixed size bin and the other with self-organizing maps (SOM) that solves the underlying dimensionality problem inherent in the first method.
Resource Allocation in Quantum-Key-Distribution-Secured Datacenter Networks with Cloud-Edge Collaboration

The problem of communication, computation, caching, and cryptographic (4C) resource allocation is first solved in quantum-key-distribution-secured datacenter networks with cloud-edge collaboration. Simulation shows great performances of the proposed solution considering blocking rate and key consumption.
A Comparative Approach for Opinion Spam Detection Using Sentiment Analysis

The most important sources of information about the products or services are online reviews. People trust online review comments while purchasing electronics items, hotel booking, college admission, movies, etc. Sometimes it has been intentionally written by the fake reviewers for monetary gain, business rivalry, etc. Many times, these messages were found fake after the judgments. The fake reviews transmission has a significant social and economic impact on society. Hence, an accurate detection mechanism must be there to identify fake reviews. In this paper, the opinion spam detection mechanism is proposed using sentiment analysis (SA) for content-based applications. In this technique, the sentiment score of the sentences is computed. It is detected as fake or not fake, depending on the sentiment score of reviews. The work also proposed a Long Short-Term Memory (LSTM) based deep learning approach to identify the topic of fake reviews. Combining these two approaches provides a more accurate opinion spam detection rate compared to other existing models. On the benchmark “Deceptive Opinion Spam Corpus v1.4” dataset is used. Our model’s accuracy is 92.46% with 9.23% of the false acceptance rate and 5.50% of the false rejection rate.
THE INFLUENCE OF SOCIAL NETWORKS ON HUMAN COMMUNICATION: ALGORITHMS AND SOCIAL

the purpose of this study is to understand how social networks and algorithms influence human communication and how their existence has conditioned the creation of social bubbles on the internet.Scientific articles that portray algorithms were used as a source of the data presented to illustrate their influence.It was observed that algorithms formed the basis for the development of networks and the personalizing of content, leading to the creation of targeted layers of information, the social bubbles.Finally, the study made it possible to understand that the so-called Social Bubbles corroborate segregation and polarization on and off the Internet.
A Scalable Multi-User QKD Hub for Entanglement-Based Phase-Time Coding

Simultaneous pairwise quantum key distribution between four parties was demonstrated by implementing an entanglement-based phase-time coding protocol with a type-0 SPDC photon source. The star-shaped network is scalable to dozens of users.
Engineering Spatial Correlations in Entangled Twin Beams

We show full control over the spatial correlations in a macroscopic quantum state of light generated via four-wave mixing. Taking advantage of momentum conservation, we engineer the distribution of the correlations via control of the angular spectrum of the pump.
Proposed Design for Data Retrieval using Efficient Algorithm

Data mining is used for finding patterns from large amount of data which is in raw format. These patterns are then analyzed to gain useful information from them. There are many branches of data mining, one of the most interesting branch is frequent item-set mining (FIM). FIM deals with finding items that are frequently brought together by customers. Like for example, if a customer purchases a mobile phone, he also tends to purchase mobile cover, ear phones etc along with it. But such kinds of patterns are not always useful to all stake-holders. Such patterns do not emphasize on the profit obtained of sale i.e. the utility obtained from product. In order to overcome this problem, the concept of high utility item-set mining (HUIM) came into existence. HUIM is used to find the utility or profit obtained from the items in transaction data. There are various algorithms for HUIM, TKU (Top K Utility) and TKO (Top K in one phase) are two well known algorithms of HUIM. The detailed study and practical analysis of these two algorithms show that there are certain drawbacks assigned with them. TKO algorithm gets executed in very less amount of time but it gives incorrect output. Whereas TKU algorithm gives accurate results when applied on database, but its execution time is very high. Hence in order to enhance the performance of these two HUIM algorithms a hybrid algorithm i.e. TKO with TKU algorithm is proposed in this paper. The two algorithms when combined give accurate result and also get executed in considerable less amount of time