Reviewerly/exHarmony huggingface dataset:
Freeing hybrid distributed AI training configuration

Deep neural network (DNN) has become the leading technology to realize Artificial Intelligence (AI). As DNN models become larger and more complex, so do datasets. Being able to efficiently train DNNs in parallel has become a crucial need. Data Parallelism (DP) is the widest-used solution today to accelerate DNN training but could be inefficient when processing DNNs with large-size parameters. Hybrid Parallelism (HP), which applies different parallel strategies on different parts of DNNs, is more efficient but requires advanced configurations. Not all AI researchers are experts in parallel computing, thus automating the configuration of HP strategies is very desirable for all AI frameworks. We propose a parallel semantics analysis method, which can analyze the trade-offs among different kinds of parallelisms and systematically choose the HP strategies with good training time performance. We demonstrated experimentally 260% speedup when applying our method compared to using a conventional DP approach. With our proposal, AI researchers would be able to focus more on AI algorithm research without being disturbed by parallel analysis and engineering concerns.
Metric Based Evaluation and Improvement of Software Designs

The demand for quality software expands as the business environment grows internationally.Common to all software development methodologies is a design phase that focuses on the physical software model.Design criteria used to evaluate these models are important for refinement and improvement.The purpose of this research is to apply transformations of McCabe's cyclomatic complexity as a means for improving software design and assessing risks within a design.In this research, two metrics, the management (C MGT ) and maintenance (C MN ) coefficients are introduced, and they are used to address the architectural, size, and quality dimensions of a software design.
The Persistence of Global Terrorism

This study investigates persistence of global terrorism in a panel of 163 countries for the period 2010 to 2015. The empirical evidence is based on Generalised Method of Moments. The following findings are established. First, persistence in terrorism is a decreasing function of income levels because it consistently increases from high income (through upper middle income) to lower middle income countries. Second, compared to Christian-oriented countries, terrorism is more persistent in Islam-oriented nations. Third, landlocked countries also reflect a higher level of persistence relative to their coastal counterparts. Fourth, Latin American countries show higher degrees of persistence when compared with Middle East and North African (MENA) countries. Fifth, the main determinants of the underlying persistence are political instability and weapons import. The results are discussed to provide answers to four main questions which directly pertain to the reported findings. These questions centre on why comparative persistence in terrorism is based on income levels, religious orientation, landlockedness and regions.
Overfitting-based Bias-generating Neural-network : Simulation of Pavlov’s Dog

Since the advent of artificial intelligence techniques, many scholars have considered the phenomenon of overfitting to be negative and have worked to eliminate it. However, there are still many scholars who believe that the phenomenon of overfitting is worth exploiting. This paper argues that the phenomenon of overfitting of neural networks can reproduce the process of biologically generated prejudice, and builds a neural network prone to overfitting and applies it to a simple model of a dog. The simulations showed that the behavior of the simulated dog was similar to the conditioned reflexes in Pavlov’s dog experiments. Therefore, this paper argues that the judicious use of overfitting techniques can better enable artificial intelligence to be “each in its way”.
Amazon’s Bezos ‘asked Alexa to play videos that ridiculed’ Trump, new book says

Amazon founder Jeff Bezos asked a prototype of the tech giant's Alexa smart-home products to play videos that ridiculed then-presidential candidate Donald Trump in 2016.
Towards Solving the Winograd Schema Challenge: Model-Free, Model-Based and a Spectrum in Between

The Winograd Schema Challenge (WSC) has attracted much attention recently as common sense is recognized to be not only the key to human-level intelligence but also a bottleneck faced by recent progress. Although neural language models (LMs) have achieved state-of-the-art (SOTA) performance on WSC, they fall short on interpretability and robustness against adversarial attacks. Contrarily, methods with structured representation and explicit reasoning suffer from the difficulty of knowledge acquisition and the rigidness of representation. In this paper, we look back on the current model-free and model-based approaches, pointing out the missing ingredients towards solving the WSC. We report our preliminary exploration of formalizing the WSC problems using a variant of first-order language and our first-hand findings of indispensable capabilities of human-level commonsense reasoning. The issues we encounter suggest that a full spectrum of representation tools and reasoning abilities are called for.
Rethinking Training Objective For Self-Supervised Monocular Depth Estimation: Semantic Cues To Rescue

Monocular depth estimation finds a wide range of applications in modeling 3D scenes. Since it is expensive to collect ground truth labels to supervise training, plenty of works have been done in a self-supervised manner. A common practice is to train the network optimizing a photometric objective (i.e., view synthesis) due to its effectiveness. However, this training objective is sensitive to optical changes and lacks a consideration of object-level cues, which leads to sub-optimal results in some cases, e.g., artifacts in complex regions and depth discontinuities around thin structures. We summarize them as depth ambiguities. In this paper, we propose an easy yet effective architecture, introducing semantic cues into supervision to solve problems mentioned above. First through our study on the problems we Figure out that they are due to the limitation of the commonly applied photometric reconstruction training objective. Then we come up with our method using semantic cues to encode the geometry constraint behind view synthesis. The proposed novel objective is more credible towards confusing pixels, also takes an object-level perception. Experiments show that without introducing extra inference complexity, our method alleviates depth ambiguities greatly and performs comparably with state-of-the-art methods on KITTI benchmark.
Application Research on Higher Vocational College Higher Mathematics Network Teaching Platform Combined with Computer in the Cloud Computing Era

Abstract Higher vocational colleges are the place to cultivate talents. In addition to a group of excellent teachers, the school also needs a large number of advanced teaching equipment resources, including hardware facilities and software resources. However, higher vocational colleges are different from ordinary enterprises. They are not for profit, and they are often not as fast as enterprises in equipment updating. At present, most of the teaching equipment in higher vocational colleges is old and aging, the laboratory is scarce, the computer hardware and software can not keep up with the development of the times, the projector pixel is low, the distribution of teaching resources is uneven. With “cloud computing”, the network and equipment of the school can be developed in an all-round, sound and fast way, It is easy to connect the server and client in the network into a database. At the same time, this technology can be applied to the teaching of advanced mathematics. It can effectively combine the comprehensive and quick methods.
New Moral Value Guidance Model for University Students under the Network Environment

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Primal and dual combinatorial dimensions

We give tight bounds on the relation between the primal and dual of various combinatorial dimensions, such as the pseudo-dimension and fat-shattering dimension, for multi-valued function classes. These dimensional notions play an important role in the area of learning theory. We first review some classical results that bound the dual dimension of a function class in terms of its primal, and after that give (almost) matching lower bounds. In particular, we give an appropriate generalization to multi-valued function classes of a well-known bound due to Assouad (1983), that relates the primal and dual VC-dimension of a binary function class.
The Program of a Pedagogical University’s Strategic Development: Analyzing the Experience

The article analyzes the case of creating a project for Ulyanovsk State Pedagogical University development. This process resulted in the adoption of a university development program until 2024. The authors provide the alternatives and the reasons for choosing the strategic type of planning the development, the type of strategic orientation of the university (market, entrepreneurial and educational orientation), and the type of its reorganization (restructuring and reconfiguration). The paper indicates the reasons and the criteria for the experts’ selection and their role in its creation. There are presented the results of the analysis of all project development stages, the work of focus groups with the main stake holders, the group work on the blocks of the program and the sessions devoted to the road maps. This case can be useful to those who are starting this kind of activity for the first time, since the authors describe the progress of work and consider its timing, as well as the difficulties encountered. The article also explains why this or that decision was made, indicates the right decisions, reveals the errors and the lessons learned.
Compositional Sketch Search

We present an algorithm for searching image collections using free-hand sketches that describe the appearance and relative positions of multiple objects <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Sketch based image retrieval (SBIR) methods predominantly match queries containing a single, dominant object invariant to its position within an image. Our work exploits drawings as a concise and intuitive representation for specifying entire scene compositions. We train a convolutional neural network (CNN) to encode masked visual features from sketched objects, pooling these into a spatial descriptor encoding the spatial relationships and appearances of objects in the composition. Training the CNN backbone as a Siamese network under triplet loss yields a metric search embedding for measuring compositional similarity which may be efficiently leveraged for visual search by applying product quantization.
Placement of Teleported Co-users in AR

Teleportation and conversations with virtual representations of remote people have been made possible by recent developments in augmented reality (AR) technology. This paper aims at understanding how such AR telecommunication systems should be implemented by asking where to display 3D scans of potential remote users. As the perfect interaction design solution may be different while walking versus while staying in one place, we conducted a user study comparing both. We also varied the placement of the remote user in the co-user’s field of view (FoV) and where the coordinate system in which the 3D scan is visualized has its origin. We found that remote users we talk to should, in general, be visualized in AR in front of us, but in situations in which the physical world requires attention a visualization in the periphery is better. Re-placing the co-user through gestures is not desired, but the ability to look away from them should be supported, which strongly supports placing virtual co-users in AR relatively to the user’s body.
The Design of Intelligent Monitoring System for City Gas Pipeline Network

Aiming at the problems of information islands and data "sleeping" in the heterogeneous systems of gas enterprises, the use of business integration methods helps achieve data sharing, business innovation and intelligent control via data mining. It improves the management efficiency of the enterprise and reduces the operating cost, which has important significance and very high practical application value for the gas enterprise.
Generating a Scene from Text for Smart Education

Students learn best when they can see the material being taught. So, instead of a traditional approach where teachers just write a sentence and it is up to students to visualize the sentence which is written, a new approach has been proposed. The system that has been proposed is, whenever a user scans the text, it will be detected and processed using Natural Language Processing (NLP). An animated scene will be created which will describe the text as appropriate as possible. This system will have a good impact on primary education. Students will be able to visualize the sentence and facts that are being taught by the teacher. The system consists of two steps. The first step is scanning the written text and converting it into actual text using OCR and then processing that text using NLP. Second step is to generate a scene which best describes the text.
Micro-CLK

Inter-process communication (IPC) has always been the "Achilles heel" of microkernels, determining their overall performance. The entire history of microkernel development is tightly coupled to the debates about IPC, its efficiency, and the bottleneck it creates. The microkernel research community has consistently viewed the idea of message passing as the fundamental foundation of the microkernel, which is considered linked to its design inextricably.
A neural network based approach to identifying COVID-19 safety regulations compliance

This paper proposes an image processing and neural network based automated solution for identifying whether or not people wear their masks properly and maintain the minimum required social distance. This system has two components: a subsystem which estimates the distance between two persons (which is performed via Haar cascades) and a subsystem which identifies whether or not a person wears a mask (which is performed via the MobileNetV2 convolutional neural network (CNN) architecture).
A Super-resolution Image Reconstruction Algorithm of Molten Droplet Based on Multi-feature Fusion Method

Containerless microgravity experiments are commonly employed to study the properties of novel materials. Researchers install an electrostatic levitation system in the vacuum drop tube to build a container-free microgravity environment. In these experiments, molten material droplets are initially electrostatically levitated at the top of the drop tube, and then released to free fall in vacuum. This effectively places the molten droplets in an ideal containerless microgravity environment. To capture the temperature and volume parameters of these molten droplets, high-speed cameras must be installed inside the drop tube. However, the quality of these images is extremely poor, the molten droplets are approximately 3 mm in diameter. To accurately obtain the physical parameters of the molten droplets, super-resolution (SR) reconstruction was used to reconstruct high-resolution (HR) images from the low-resolution (LR) high-speed camera images. Our model also incorporated a residual network (ResNet) and dense network (DenseNet)architecture, which increases the network depth as well as learning capacity. Furthermore, the use of the ResNet and DenseNet architecture expands the receptive field, which increases the amount of contextual information that can be extracted from the LR images, and improves the effect of feature fusion. Sub-pixel convolution is used to perform pixel fusion to create feature maps in the upscaling stage, following which the extracted feature maps were organized to generate suitably magnified SR images. The experimental results show that our proposed SR method for molten droplet image analysis is superior to other baseline SR methods in a number of metrics, including accuracy and peak signal-to-noise ratio.
Biomedical Text Summarization Based on the Itemset Mining Approach

The expanding amount of text-based biomedical information has prompted mining valuable or intriguing frequent patterns (words/terms) from extremely massive content, which is still a very challenging task. In the chapter, the authors have conceived a practical methodology for text mining dependent on the frequent item sets. This chapter presents a strategy utilizing item set mining graph-based summarization for summing up biomedical literature. They address the difficulties of recognizing important subjects or concepts in the given biomedical document text and display the relations between the strings by choosing the high pertinent lines from biomedical literature using apriori itemset mining algorithm. This method utilizes essential criteria to distinguish the significant concepts, events, for example, the fundamental subjects of the input record. These sentences are determined as exceptionally educational, applicable, and chosen to create the final summary.
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments

Reinforcement Learning (RL) is known to be often unsuccessful in environments with sparse extrinsic rewards. A possible countermeasure is to endow RL agents with an intrinsic reward function, or ‘intrinsic motivation’, which rewards the agent based on certain features of the current sensor state. An intrinsic reward function based on the principle of empowerment assigns rewards proportional to the amount of control the agent has over its own sensors. We implemented a variation on a recently proposed intrinsically motivated agent, which we refer to as the ‘curious’ agent, and an empowerment-inspired agent. The former leverages sensor state encoding with a variational autoencoder, while the latter predicts the next sensor state via a variational information bottleneck. We compared the performance of both agents to that of an advantage actor-critic baseline in four sparse reward grid worlds. Both the empowerment agent and its curious competitor seem to benefit to similar extents from their intrinsic rewards. This provides some experimental support to the conjecture that empowerment can be used to drive exploration.
Integrating usage monitoring for continuous evaluation and testing in the UI of an industry application

Developing interactive systems and testing with realistic scenarios requires a detailed understanding of how these systems are used in their real environment. In this paper, we report on our experience from implementing a usage monitoring approach for the touch-enabled human machine interface of an industrial machine. The approach supports automated recording of user interface events as basis for analyzing interactions of users with the system. It collects information about navigation paths to different screens, activities on these screens, and the usage of functionality provided by the application. We evaluated three different approaches to integrate the required usage monitoring into the UI, considering aspects such as necessary changes to the existing code base, dependencies to third-party libraries, and the entailed performance overhead. The paper provides a detailed description of the implementation of the selected approach and a discussion of the lessons we learned from integrating the monitoring in an existing application.
Application-Agnostic Spatio-Temporal Hand Graph Representations For Stable Activity Understanding

Using hand skeleton data to understand complex hand actions, such as assembly tasks or kitchen activities, is an important yet challenging task. This paper introduces an unsupervised hand graph-based spatio-temporal feature extraction method. To evaluate the efficacy of the proposed representation, we consider action segmentation and recognition tasks. The segmentation problem involves an assembling task in an industrial setting, while the recognition problem deals with kitchen and office activities. For both tasks, we propose novel notions of stability, loss function stability (LFS) and estimation stability with cross-validation (ESCV), that are used to quantify the robustness of achieved solutions. Our proposed feature extraction leads to classification performance comparable to state of the art methods, while achieving significantly better accuracy and stability in a cross-person setting. The proposed method also outperforms the existing methods in the segmentation task in terms of accuracy and shows robustness to any change in the input hyper-parameters.
Ability of Solving Augmented Reality Problems with Fuzzy Logic Algorithms

Augmented reality has long been part of both the industrial sphere and ordinary life. However, there are still many difficulties associated with the correct operation of this modeling area, the problems of hardware implementation being closely related to the problems of the software part. The implementation of the pattern recognition algorithm plays a huge role here, the solution to the problems of which can be the introduction of fuzzy logic methods.
Intuitionistic Fuzzy Temporal-Modal Operators

New type of intuitionistic fuzzy operators that are extensions similar to the intuitionistic fuzzy modal operators, as well as to the temporal operators defined over intuitionistic fuzzy sets (IFSs), are introduced and some of their basic properties are discussed. For the first time, the definitions of the IFS-forms of intuitionistic fuzzy implications $$\rightarrow _{139}, ..., \rightarrow _{185}$$ are given. Some ideas for future research are described.
Optimization of a Composition Microprogram Control Unit with Elementary Circuits

Introduction. The control unit coordinating interaction of all other blocks of a digital system is one of the central blocks and is a sequential circuit. As a rule, when synthesizing control unit circuits, the problem arises of reducing hardware costs. Methods for solving this problem depend on features of both the architecture of the control unit and the elemental basis. Purpose. The main goal of this work is to reduce hardware costs and power consumption of control units of digital systems by taking into account features of the element base of the control unit and rational organization of addressing microinstructions. FPGA (field-programmable logic array) microcircuits, widely used for the implementation of modern digital systems, were chosen as an elementary basis. Methods. Methods of set theory, synthesis of automata, and software modeling as well as the library of standard automata and FPGA Virtex-7 from Xilinx were used for assessment the effectiveness of solving the problem. Results. The paper proposes a method for optimizing the circuit of the microinstruction addressing unit based on splitting the set of outputs of elementary linear operator circuits, which is based on the idea of double coding of states. The proposed method, under certain conditions, makes it possible to reduce the number of levels in the microinstruction addressing circuit to two. Conclusion. Studies have shown that double coding of states can increase performance, reduce hardware costs (the number of LUTs and their interconnections) and power consumption in Mealy’s circuitry. Based on these results, it can be expected that, with the number of conditions exceeding the number of LUT inputs, the proposed approach will improve the characteristics of the composition microprogram control unit in comparison with the equivalent control unit U1.