Reviewerly/exHarmony huggingface dataset:
Detect and Remove Watermark in Deep Neural Networks via Generative Adversarial Networks

Deep neural networks (DNN) have achieved remarkable performance in various fields. However, training a DNN model from scratch requires expensive computing resources and a lot of training data, which are difficult to obtain for most individual users. To this end, intellectual property (IP) infringement of deep learning models is an emerging problem in recent years. Pre-trained models may be stolen or abused by illegal users without the permission of the model owner. Recently, many works have been proposed to protect the intellectual property of DNN models. Among these works, embedding watermarks into DNN based on backdoor is one of the widely used methods. However, the backdoor-based watermark faces the risk of being detected or removed by an adversary. In this paper, we propose a scheme to detect and remove backdoor-based watermark in deep neural networks via generative adversarial networks (GAN). The proposed attack method consists of two phases. In the first phase, we use the GAN and few clean images to detect the watermarked class and reverse the watermark trigger in a DNN model. In the second phase, we fine-tune the watermarked DNN with the reversed backdoor images to remove the backdoor watermark. Experimental results on the MNIST and CIFAR-10 datasets demonstrate that, the proposed method can effectively remove watermarks in DNN models, as the watermark retention rates of the watermarked LeNet-5 and ResNet-18 models reduce from 99.99% to 1.2% and from 99.99% to 1.4%, respectively. Meanwhile, the proposed attack only introduces a very slight influence on the performance of the DNN model. The test accuracy of the watermarked DNN on the MNIST and CIFAR-10 datasets drops by only 0.77% and 2.67%, respectively. Compared with existing watermark removal works, the proposed attack can successfully remove the backdoor-based DNN watermarking with fewer data, and can reverse the watermark trigger and the watermark class from the DNN model.
Short-term load forecasting based on depth measurement information

Abstract According to the predicted time span, the power load forecasting problem can be roughly divided into long-term and short-term forecasting. Among them, ultra-short-term load forecasting generally outputs load changes in the next few minutes to for on-line monitoring of power equipment, while short-term load forecasting mainly refers to pre-day load forecasting and pre-week load forecasting. It provides reference for hydropower dispatching, unit start and stop, water-fire coordination and so on, and is the basic work needed for the daily operation of power grid. In this paper, long and short memory networks network is used to predict short-term load, and depth measurement information is used as neural network input to construct mapping relationship based on load sequence timing.
Association Rule Mining using FP-Growth Algorithm to Prevent Maverick Buying

Maverick Buying describes unwelcome behaviour in the field of corporate purchasing management. It is a purchasing made outside the purchasing and procurement processes. Neddy Enterprise Sdn. Bhd (NE) is an electrical company that often takes a project from a government agency. NE needs to buy supplies to use them in a specific project. However, the purchasing and operational strategy are not fully optimised, where the maverick buying process has been identified in the initial phase of the study. When NE received a project from their customer in the regular operation, the project supervisor will decide the supplies needed. However, that did not happen in NE. From January to December 2019, some supplies have been purchased for every month in a small quantity. This paper aims to help NE in making decisions, especially in planning for their supply purchasing. Association rule mining has been used to associate the relationship between the supply needed and the projects acquired. Cross-Industry Standard Process for Data Mining (CRISP-DM) has been used as a project framework to carry out this research. As a result, several supplies are associated with a specific type of project. This finding will help NE to make better purchasing planning.
IMPLEMENTATION OF PRE-TRAINED DEEP LEARNING MODEL FOR DOG BREED CLASSIFICATION

Image classification made tremendous advancement with improved techniques and its accuracy continuously improves. However when comes to fine-grained classification, huge scope of improvement. It is easy to identify the various animals from the image, but not easy to identify the animal breed. In this paper, effort is made to improve the classification of animal breed. Standard Stanford dog breed dataset is used to train and test various pre-trained deep learning models. The effort is made to fine-tune pre-train network and compare the result.
PixelTransformer: Sample Conditioned Signal Generation

We propose a generative model that can infer a distribution for the underlying spatial signal conditioned on sparse samples e.g. plausible images given a few observed pixels. In contrast to sequential autoregressive generative models, our model allows conditioning on arbitrary samples and can answer distributional queries for any location. We empirically validate our approach across three image datasets and show that we learn to generate diverse and meaningful samples, with the distribution variance reducing given more observed pixels. We also show that our approach is applicable beyond images and can allow generating other types of spatial outputs e.g. polynomials, 3D shapes, and videos.
NOT-AT-ISSUE CONTENT IN THE REFLEXIVE-REFERENTIAL THEORY

ABSTRACT The Reflexive-Referential Theory is a multi-content approach to utterance interpretation. Its main proponent, John Perry, assumes that utterances of sentences with singular terms express several contents, depending on how their utterers and interpreters harness information from the situations in which they are produced. However, the theory says little to nothing about implied content, like presuppositions. Here, I discuss the possibility of including presuppositions, defined in terms of the concept of not-at-issue content, in this view. I begin with a brief characterization of the Reflexive-Referential theory in what regards its theoretical motivations and main thesis, followed by a study case of presuppositional not-at-issue content associated to a specific class of singular terms, proper names. To conclude, I ponder over a few consequences of this overall project of theoretical expansion.
Analytical review of approaches to integration of software systems

Conducted comparison analysis of existing approaches to integrations of software systems, with the purpose of continuous design of new alternative approaches for resolving integration tasks and web-service composition tasks. Conducted integration of payment system with online store with one of the mentioned approaches for a deeper understanding of problems that caused during the integrations of a software system. Were considered challenges that actual for integration developers during the integrations and methods for resolving raised issues. Defined the key moments of integrations of software systems and interactions between them that we should pay attention to. The subject of research needs a new point s of view to the issue and new alternative approaches that might bring more flexibility, increase performance, and they will find the right place for applying in the field of integrations of software systems and the composition of web-services.
ES-ENAS: Controller-Based Architecture Search for Evolutionary Reinforcement Learning

We introduce ES-ENAS, a simple yet general evolutionary joint optimization procedure by combining continuous optimization via Evolutionary Strategies (ES) and combinatorial optimization via Efficient NAS (ENAS) in a highly scalable and intuitive way. Our main insight is noticing that ES is already a highly distributed algorithm involving hundreds of forward passes which can not only be used for training neural network weights, but also for jointly training a NAS controller, both in a blackbox fashion. By doing so, we also bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different yet common lines of research. We demonstrate the utility and effectiveness of our method over a large search space by training highly combinatorial neural network architectures for RL problems in continuous control, via edge pruning and quantization. We also incorporate a wide variety of popular techniques from modern NAS literature including multiobjective optimization along with various controller methods, to showcase their promise in the RL field and discuss possible extensions.
Exposing model bias in machine learning revisiting the boy who cried wolf in the context of phishing detection

Grown out of the quest for artificial intelligence (AI), machine learning (ML) is today’s most active field across disciplines with a sharp increase in applications ranging from criminology to fraud detection and to biometrics. ML and statistics both emphasise model estimation/training and thus share the inescapable Type 1 and 2 errors. Extending the concept of statistical errors into the domain of ML, we devise a ground-breaking pH scale-like ratio and intend it as a litmus test indicator of ML model bias completely masked by the popular performance criterion of accuracy. Using publicly available phishing dataset, we conduct experiments on a series of classification models and consequently unravel the significant cost implications of models with varying levels of bias. Based on these results, we recommend practitioners exercise human judgement and match their own risk tolerance profile with the bias ratio associated with each ML model in order to guard against potential unintended adverse effects.
Research on Construction Risk Management of Subway Project Based on Bayesian Network

The data on construction safety risk accidents in China in recent years are collected and sorted out. The main risk factors in Xi'anlu station of the Dalian subway phase I project are determined by the expert investigation method. According to the causal relationship among risk factors, the corresponding Bayesian network model is determined. The construction risk occurrence probability of this project can be predicted by using forward causal inference technology based on the Bayesian network, and construction risk can be diagnosed by using reverse inference technology. The results show that the risk probability of the construction safety of this subway project is 0.530. The data shows that the overall risk level of construction safety of this subway project is medium. The key factors that make the Xi'anlu station of the Dalian subway phase I project in a dangerous state are inaccurate construction measurement data, non-detailed construction organization or construction scheme, and unreasonable connection of the operation process.
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems

Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. 2021.
Kossinna, the Nordische Gedanke, and Swedish Archaeology: Discourse and politics in German and Swedish archaeology 1900-1950

This article looks at the relation between Swedish and German archaeology in the first half of the 20th century. In particular, the focus is on the question why Swedish prehistorians came to accept the archaeology of Gustaf Kossinna. Thc methodological inspiration principally comes from Carlo Ginzburg and his idea of clucs. The empirical material mainly consists of books and journals. Newspapers and archives have also been used, but to a much lesser cxtent. Gustaf Kossinna was the dominant archaeologist in Germany in the early 20th ccntury. The Siedliungsarchäologie was to a large extent his creation, and he was instrumental in making a spatial tum in archaeology. Kossinna was part of the Nordische Gedanke movement. Thus, the Nordic/Germanic blood, earth, and soil were paramount to Kossinnian archaeology. It was so important that Kossinna thought only Europe, in particular the Nordic region, which to him included parts of Germany, should be subjectcd to archaeological investigations. At the tum of the 20th ccntury, Swedish archaeology was dominated by Oscar Montelius and his chronological typology. In the 1910s, however, his position was challcngcd by proponents of a more spatially oriented archaeology. Among the opponents werc two young archaeologists, Nils Åberg and Sune Lindquist, who both had participated in the archaeological seminar organiscd by Knut Stjerna. In different ways, Åberg and Lindquist used Kossinna's archaeology in thc struggle against the Montelian dominance.
JPS+

Jump point search (JPS) is a devised optimal pathfinding algorithm that can speed up searches on uniform cost grid maps by up to an order of magnitude over traditional A*. Both JPS and JPS+ use a state-space pruning strategy that only works for grid search spaces where the cost of traversal is uniform with regard to distance. This chapter explains in detail how JPS+ works and the exact specifics on how to implement it. The genius of the preprocessed map is that it contains many of the decisions required for the search, thus making the runtime code much simpler and faster than traditional JPS. JPS+ takes a great deal of the runtime computation from JPS and stores it directly in the map. As a result, the algorithm is up to an order of magnitude faster than traditional JPS and two orders of magnitude faster than a highly optimized A*.
Organization of the education process based on a credit system, advantages and prospects

This article to the analysis of credit system introduction to the education process, it's futures and perspectives in educational system of the Republic of Uzbekistan, especially in TUIT is devoted. This article the fundamental position in the modern assessment system in the field of education. The introduction of a credit system raises the need to compare similar modernization steps in the experience of other countries are describes. The introduction of the credit system in Uzbekistan requires a comprehensive analysis of the optimal approach in the process of its implementation. This the article reflects the educational practice in countries with economies in transition regarding the introduction of a credit system for assessing knowledge gained on the basis of experimental work.
Rationalizing Resource Utilization in Cloud Computing Using Coalition Formation Strategy

Even though the contribution of cloud computing towards the Sustainable Development (SD) of communities is still under research investigation, cloud computing has become an integral part of many ICT solutions that shape our daily lives.Thus, some researchers recommend taking considerable actions to point cloud computing development towards supporting SD.In this research, an approach to designing energy efficient cloud architecture as a way of supporting SD is proposed.Resource allocation is a challenging process in cloud management, the goal is to allocate the exact amount of resources needed throughout the service duration; tight enough to avoid unnecessarily wasting resources and loose enough to prevent any degradation in Quality of Service (QoS) that may lead to the violation of the Service Level Agreement (SLA) between the service provider and the cloud user.This study aims to achieve the desired balance by benefiting from the history of the user's behaviour and from sharing resourcesmore specifically Virtual Machines (VM)among a coalition of users.Coalition formation strategy is used to build groups of cloud users based on their cloud behaviour history.Users are grouped in a way that their usage patterns complement each other, either to avoid the loss stemming from VM excess reserved space or from idle times.A type of architecture that fulfils this improvement process is proposed and implemented on Google Compute Engine (GCE).The contribution of this research is that it applies the Coalition formation strategy in cloud computing resource management in a novel way and experiments show that there are scenarios where the efficiency of resource management has improved.Evaluation of the performance of the proposed architecture is done by comparing resource utilization for both the cloud following this architecture and the cloud that runs the basic GCE strategy.In conclusion, it is observed that improvements depend on accuracy of the prediction of usage pattern of the user.Results show that in certain scenarios, improvements can be made to up to 24% of VM usage and, in other scenarios, it can minimize the number of required VMs, thus contributing to green computing.
Bank predictions for prospective long-term deposit investors using machine learning LightGBM and SMOTE

Abstract Banks try to get profit from society in various ways. One way is to use long-term deposit investment offers. If the product offering process for potential investors is not carefully considered, it will waste resources. Therefore, this study analyzes the accuracy of the predictions of consumers who have a high chance of participating in this program. The dataset used is historical bank data provided by Kaggle. In previous research, accuracy prediction has been carried out, but the accuracy is still low because it does not use a method to balance the class. Better accuracy can be improved using LightGBM and SMOTE methods. The test results with the number of testing data as much as 6590 and training data as many as 32950 show the highest accuracy of 90.63%.
HIGHER EDUCATION CHALLENGES CAUSED BY COVID-19

The conference proceedings are focused on the study of trends in development of innovative scientific research in the context of global changes. General issues of the engineering, philological, pedagogical, legal, economic sciences, history of art, agriculture and so on are considered. The publication is designed for scientists, lecturers, postgraduate students, students, as well as for the general readers.International Scientific Conference Trends in Development of Innovative Scientific Research in the Context of Global Changes: Conference Proceedings, May 7-8, 2021. Riga, Latvia: «Baltija Publishing». 132 pages.
An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models

This work demonstrates the development process of a machine learning architecture for inference that can scale to a large volume of requests.In our experiments, we used a BERT model that was fine-tuned for emotion analysis, returning a probability distribution of emotions given a paragraph.The model was deployed as a gRPC service on Kubernetes.Apache Spark was used to perform inference in batches by calling the service.We encountered some performance and concurrency challenges and created solutions to achieve faster running time.Starting with 3.3 successful inference requests per second, we were able to achieve as high as 300 successful requests per second with the same batch job resource allocation.As a result, we successfully stored emotion probabilities for 95 million paragraphs within 96 hours.
Speech acoustics and mental health assessment

According to the World Health Organization, more than 264 million people worldwide suffer from Major Depression Disorder (MDD) and another 20 million have schizophrenia. MDD and schizophrenia are among the most common precursors to suicide and, according to a 2018 CDC report, suicidality is the second leading cause of death in youth and young adults between 10 and 34 years of age. While suicidality has historically been at low rates in the black community, it has recently become a crisis for black youth. It is the second leading cause for the death of black children between 10 and 14 years of age, and it is the third leading cause of death for black adolescents between 15 and 19 years of age. Our work is focused on understanding how a person's mental health status is reflected in their coordination of speech gestures. Speech articulation is a complex activity that requires finely timed coordination across articulators, i.e., tongue, jaw, lips, velum, and larynx. In a depressed or a psychotic state, this coordination changes and, in turn, modifies the perceived speech signal. In this talk, I will discuss a speech inversion system we developed that maps the acoustic signal to vocal tract variables (TVs). The trajectories of the TVs show the timing and spatial movement of speech gestures. Next, I will discuss how we use machine learning techniques to compute articulatory coordination features (ACFs) from the TVs. The ACFs serve as an input into a deep learning model for mental health classification. Finally, I will illustrate the key acoustic differences between speech produced by subjects when they are mentally ill relative to when they are in remission and relative to healthy controls. The ultimate goal of this research is the development of a technology (perhaps an app) for patients that can help them, their therapists and caregivers monitor their mental health status between therapy sessions.Q&amp;A will follow the presentation
Versatile Verification of Tree Ensembles

Machine learned models often must abide by certain requirements (e.g., fairness or legal). This has spurred interested in developing approaches that can provably verify whether a model satisfies certain properties. This paper introduces a generic algorithm called Veritas that enables tackling multiple different verification tasks for tree ensemble models like random forests (RFs) and gradient boosting decision trees (GBDTs). This generality contrasts with previous work, which has focused exclusively on either adversarial example generation or robustness checking. Veritas formulates the verification task as a generic optimization problem and introduces a novel search space representation. Veritas offers two key advantages. First, it provides anytime lower and upper bounds when the optimization problem cannot be solved exactly. In contrast, many existing methods have focused on exact solutions and are thus limited by the verification problem being NP-complete. Second, Veritas produces full (bounded suboptimal) solutions that can be used to generate concrete examples. We experimentally show that Veritas outperforms the previous state of the art by (a) generating exact solutions more frequently, (b) producing tighter bounds when (a) is not possible, and (c) offering orders of magnitude speed ups. Subsequently, Veritas enables tackling more and larger real-world verification scenarios.
Simple and Effective VAE Training with Calibrated Decoders

Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning to determine the optimal amount of information retained by the latent variable. We study the impact of calibrated decoders, which learn the uncertainty of the decoding distribution and can determine this amount of information automatically, on the VAE performance. While many methods for learning calibrated decoders have been proposed, many of the recent papers that employ VAEs rely on heuristic hyperparameters and ad-hoc modifications instead. We perform the first comprehensive comparative analysis of calibrated decoder and provide recommendations for simple and effective VAE training. Our analysis covers a range of datasets and several single-image and sequential VAE models. We further propose a simple but novel modification to the commonly used Gaussian decoder, which computes the prediction variance analytically. We observe empirically that using heuristic modifications is not necessary with our method.