Reviewerly/exHarmony huggingface dataset:
Transfer of DevOps to Multi-Disciplinary Systems

DevOps integrates software development with operations [1] which basically means that agile software development is extended with continuous update and delivery of software products in operation [1]. This integration aims at generating value as early as possible by continuously releasing software products to customers and innovating these products based on feedback from operation. Today, DevOps is applied mainly in software development and significant benefits have been reported after its introduction so far in, e.g., revenue increase, reduction of time to market, or product quality improvements [2][3].In this paper, the transfer of DevOps from software product development to multi-disciplinary systems is discussed. One of the main requirements for implementing DevOps is, however, the continuous integration of development and operation processes. And, therefore, the integration of various and heterogenous discipline-specific models poses one of the main challenges for DevOps for systems. Based on a short summary of system-specific challenges, we suggest a general approach for facilitating integration as well as DevOps for systems: use of system architecture as a central model for integration with all other discipline specific models. Since a tight integration of all models is difficult, the linked data approach [4] is suggested.The paper is organized as follows: As an introduction, we present selected general challenges in systems engineering and give a short introduction in DevOps which directly addresses these challenges. On this basis, the transfer of DevOps to systems engineering is explained. Subsequently, main barriers are identified and a general concept for DevOps for systems is suggested. The paper closes with a short summary of necessary implementation steps towards DevOps for systems and an outlook on further work.
Optical Neural Networks with Arbitrary and Wideband Photonic Functionality

We demonstrate a highly scalable silicon photonic neural network architecture enabling arbitrarily complex, on-chip optical functionality. We use this architecture to demonstrate wideband power splitters, achieving near-lossless and flat-top transmission bands.
Polarization Characteristics of a Single Photon via Two Qubit Quantum State Tomography

We demonstrate the Faraday rotation of single photon, generated from heralded down converted non-linear optical process. The state estimation is performed by two qubit quantum state tomography for temperature range 8–100 K and results are analyzed through minimization algorithms to extract Faraday rotation and ellipticity angles which are mapped on the Bloch sphere.
Inferring Image Background from Text Description

Over the past five years, text-to-image synthesis has achieved impressive results. However, the generated images are still produced in low resolution and suffer from indeterminacy. In order to improve this situation, we split the image synthesis task into separate parts with predicting the layout of the scene as one of these tasks. We believe that it could be started with the background, which is usually determined by the location of the scene. Given a text, we can try to infer the location described or implied by it. In this paper, we propose two methods for obtaining this information from a given text. The first method called LET (Location Extraction Transformer) is intended to extract the words from a text, in which the scene location is directly mentioned. The second method we propose we call LIT (Location Inference Transformer). It is intended to infer the scene location which is implied by the text, but not mentioned directly. We have also collected two datasets to train and test the corresponding models. These datasets are publicly available at kaggle.com . We have compared the performance of our algorithm with several existing approaches which might be used for extracting the location information from the text. The results obtained by both LET and LIT have occurred to be more superior to other algorithms.
Intelligent Fuzzy System for Stray Pet Care Appliances

Set of automated systems have been developed for solving various industrial and social problems using different innovative technologies and intelligent techniques. One of the such important problem is the designing intelligent system that will improve human interaction and communication with stray pets. There are limited number of researches considering the design of such communication technologies. In this paper, we proposed an automated intelligent system for feeding of stray pets. This intelligent feeding system is designed using Fuzzy set theory. The structure of fuzzy feeding system is proposed. The fuzzy rule base is designed for feeding system. The Mamdani type if-then fuzzy rules are used for development of rule base. Using compositional rules of inference, a decision has been made about volume of foods and water given to stray pets. The designed system that controls the feeding and water drinking process of stray pets is implemented in the Veterinary faculty of Near East University. The implemented system satisfies the needs of the owners.
Using the IAB Contents Taxonomy and Optimal Lifting for Efficient Audience Extension

Insufficient audience size is a very common problem in targeted digital advertising. Current approaches to audience extension frequently lead to much diminishing quality metrics. This is the case, for example, for so-called look-alike techniques. We present a novel method for efficient extension of target audiences. Our base is a popular taxonomy of user interests, the IAB Contents taxonomy, combined with representation of browsing behavior of millions of users by fuzzy sets of visited IAB taxonomy segments, that are leaves of the taxonomy tree. Our method involves an algorithm for optimally lifting individual fuzzy leaf sets into a higher rank taxonomy node, a so-called “head subject”. The head subject must cover the input fuzzy leaf set in such a way that the number of errors is minimized. This algorithm was proposed as an intelligent information retrieval tool. It can be applied, however, to a very different task of targeted advertisement. To extend the audiences of a targeted advertisement, we find their head subjects off-line. Given a set of taxonomy segments corresponding to targeted audiences, we include a user as a target if her head subject covers any of those segments. It appears, in our experiments, this lifting-based step does increase the number of successful matches between user segments and campaign segments two- or three-fold without losing in the targeting quality, the click-through rate, CTR. This is in stark contrast to the conventional methods for increasing the audience numbers by reducing the admissibility thresholds, which lead to dramatic decrease in CTR.
Analisis Pola dan Dampak Serangan Cryptojacking dengan Menggunakan Metode Analisis Dinamis dan Analisis Statis

In the current era of globalization, people's daily activities are highly dependent on technology. The development of this technology is certainly utilized by many different parties. In addition to taking advantage of the positive side, there are also parts that take advantage of the negative side. In this case it is like developing criminal software or what is commonly called cybercrime. One form of this crime is cryptojacking. Cryptojacking is the dark side of cryptocurrency mining and poses a threat to website visitors or end-users. This research was carried out with a 6-step method which resulted in the result that based on static and dynamic malware analysis, the installation of Crackonosh itself runs by replacing important files on the Windows system and abusing Windows Safe Mode to damage the order and defense of the Windows system
Implementasi Bug Tracking System dengan Metodologi Scrum dan Algoritma Cosine Similarity

A bug is a system error that causes a mismatch between user expectations to actualization. Bugs often arise from programmatical mistakes, and a prompt resolution is mandatory so that the users’ business processes are assured. However, the author has noted a low efficiency of developers’ bug solving. After research, the cause is the lack of knowledge management, especially in bug tracking. This has resulted in the repetition of the error. The developers often made the same mistake that the others had encountered. The above has moved the author to build a bug tracking system (BTS) that can enhance the efficiency of bug solving. The author has developed the application using Django, MySQL, and Scrum. Scrum’s agile approaches enable dynamic and rapid development. In addition, BTS utilized a content-based filtering algorithm known as Cosine Similarity. This study results in the implementation of BTS with comprehensive capabilities. Users can manage bugs and compare them to others to find potential duplication into minimizing data redundancy. The author has implemented the BTS towards groups of developers. Through a user acceptance survey, the author finds 78.72% of system objectives achieved. The users find improvement in the quality and efficiency of their bug solving
DCAEAD: Denoising system for assisted driving images applying an autoencoder architecture

The purpose of this research work is to develop a system to correct the noise present in images, using computer vision and automatic learning tools, specifically, focused on processing images used by assisted driving systems, in which the presence of noise represents a loss of information, directly affecting the efficiency of such systems.To achieve the above, a methodological process of analysis, design, and evaluation was followed, thus obtaining a neural network model capable of responding to the proposed task, which was verified by performing tests and applying metrics such as PSNR and SSIM, observing that the system can increase up to 14 dB the PSNR value and approximately 0.76 the SSIM value concerning the initial one, this specifically for the case of the tests with Gaussian noise, while for the Salt & Pepper noise the PSNR value increased by 8.53 dB and approximately 0.73 the SSIM value concerning the initial one.
Analysis of the criteria for comparing Systems Engineering and agile methods

Increasingly complex products and the resulting need for interdisciplinary development are challenges that should be focused on to ensure a company's competitiveness. Based on this, the development of new products and technologies related to for example sustainability and digitalization requires new approaches. Given this background, agile methods and Systems Engineering (SE) are experiencing great popularity. While there are first combined approaches, the fundamental differences and similarities between Agile and SE have not yet been adequately illuminated. To analyze these trends and tackle the challenges, the aim of this paper is to enable a qualified comparison of SE and agile methods. Therefore, we examine criteria to compare Agile and Systems Engineering approaches. First, a definition and discussion of both Systems Engineering and agile product development is given. An analysis of the literature has shown that a complete set of relevant comparison criteria between the two approaches does not exist. Therefore, a complete set of comparison criteria has been formed from the literature and the experience of experts. Ultimately, the criteria proposed in this paper create a basis for a subsequent comparison.
Findings of Biomedical Russian to English Machine Translation Competition

Translation of medical texts is a complex task that requires usage of models specifically trained for the biomedical domain. In this paper we present the dataset, metrics, and baselines used for the Biomedical Russian-English Machine Translation competition. We describe dataset collection and markup along with baseline development and participants’ models. The collected dataset is available by the link https://github.com/MariaFjodorowa/MedMTEval/tree/main/data .
Survey on Design of Digital FIR Filters using Optimization Models

As the discipline of Digital Signal Processing develops, digital filters play an increasingly vital role in modern technology (DSP). The FIR filter, which stands for finite impulse response, is the most common type of filter. As a result of its versatility, FIR filters find widespread application in many fields, including image filtering, frequency modulation, precision arithmetic, and many more. For this reason, digital FIR filters are designed using various optimization techniques. Using various optimization strategies yields the best results when optimizing for different filter coefficients (concerning control parameters, dependence, premature convergence, etc.). They're advantageous due to several factors, including their straightforward implementation, low error function, high-quality searching ability, and rapid convergence. In this paper, we have covered the topic of designing efficient digital filters for signal, image, and video processing using various optimization techniques.
AI-Job Scheduling on Systems with Renewable Power Sources

The proliferation of edge data centers and the application of exhaustive tuning methods for machine learning with the use of large training data sets lead to increased power consumption of data centers. Even when considering improved operational efficiency, the conservative estimate of the power requirement for data centers in the year 2030 stands at 900 TWh. To comply with present political requirements, renewable energy sources must provide most of this power. The high volatility of these sources produces additional operational constraints for the data centers. In addition, the share of AI training applications is expected to increase in the years to come. On the one hand, those applications often require more data resources than typical interactive applications while on the other hand, they better tolerate execution delays. To exploit this tolerance, we represent it by introducing deadlines for the jobs and present simple scheduling algorithms that use this tolerance to better consider power constraints. To examine those scheduling algorithms in a practical environment, we execute experiments with existing cloud traces. Since there are no traces available that include a significant portion of AI training jobs with explicitly specified timing flexibility, we use traces from Google clusters, extract the available information, and add any missing information subject to assumptions that are reasonable in practice. We specify in detail our motives for the extension of the trace data. Our experimental results show that in practical cases, a greedy acceptance approach has a better performance than a threshold based job acceptance algorithm although the latter has a significantly better worst case performance. We obtain the best results when combining greedy acceptance with a biased load allocation strategy instead of using load balancing. Since some jobs may be highly parallel and rigid, we additionally consider an allocation algorithm that minimizes the intermediate idle times in order to achieve the best use of the available power. While the last algorithm achieves a small improvement over the other allocation schemes, it needs a significantly increased computational effort. The same is true when allocating jobs with the well known backfilling approach since deadlines require using the conservative variant of backfilling.
Using Kubernetes in Academic Environment: Problems and Approaches

In this work, we discuss our experience when utilizing the Kubernetes orchestrator (K8s) to efficiently allocate resources in a heterogeneous and dynamic academic environment. In the commercial world, the “pay per use” model is a strong regulating factor for efficient resource usage. In the academic environment, resources are usually provided “for free” to the end-users, thus they often lack a clear motivation to plan their use efficiently. In this paper, we show three major sources of inefficiencies. One is the users’ requirement to have interactive computing environments, where the users need resources for their application as soon as possible. Users do not appreciate waiting for interactive environments, but constantly keeping some resources available for interactive tasks is inefficient. The second phenomenon is observable in both interactive and batch workloads; users tend to overestimate necessary limits for their computations, thus wasting resources. Finally, Kubernetes does not support fair-sharing functionality (dynamic user priorities) which hampers the efforts when developing a fair scheme for Pod/job scheduling and/or eviction. We discuss various approaches to deal with these problems such as scavenger jobs, placeholder jobs, Kubernetes-specific resource allocation policies, separate clusters, priority classes, and novel hybrid cloud approach. We also show that all these proposals open interesting scheduling-related questions that are hard to answer with existing Kubernetes tools and policies. Last but not least, we provide a real workload trace from our installation to the scheduling community which captures these phenomena.
RARE: Renewable Energy Aware Resource Management in Datacenters

The exponential growth in demand for digital services drives massive datacenter energy consumption and negative environmental impacts. Promoting sustainable solutions to pressing energy and digital infrastructure challenges is crucial. Several hyperscale cloud providers have announced plans to power their datacenters using renewable energy. However, integrating renewables to power the datacenters is challenging because the power generation is intermittent, necessitating approaches to tackle power supply variability. Hand engineering domain-specific heuristics-based schedulers to meet specific objective functions in such complex dynamic green datacenter environments is time-consuming, expensive, and requires extensive tuning by domain experts. The green datacenters need smart systems and system software to employ multiple renewable energy sources (wind and solar) by intelligently adapting computing to renewable energy generation. We present RARE (Renewable energy Aware Resource management), a Deep Reinforcement Learning (DRL) job scheduler that automatically learns effective job scheduling policies while continually adapting to datacenters’ complex dynamic environment. The resulting DRL scheduler performs better than heuristic scheduling policies with different workloads and adapts to the intermittent power supply from renewables. We demonstrate DRL scheduler system design parameters that, when tuned correctly, produce better performance. Finally, we demonstrate that the DRL scheduler can learn from and improve upon existing heuristic policies using Offline Learning.
Catch Recognition in Automated American Football Training Using Machine Learning

In order to train receivers in American football in a targeted and individual manner, the strengths and weaknesses of the athletes must be evaluated precisely. As human resources are limited, it is beneficial to do it in an automated way. Automated passing machines are already given, therefore the motivation is to design a computer-based system that records and automatically evaluates the athlete's catch attempts. The most fundamental evaluation would be whether the athlete has caught the pass successfully or not. An experiment was carried out to gain data about catch attempts that potentially contain information about the outcome of such. The experiment used a fully automated passing machine which can release passes on command. After a pass was released, an audio and a video sequence of the specific catch attempt was recorded. For this purpose, an audio-visual recording system was developed which was integrated into the passing machine. This system is used to create an audio and video dataset in the amount of 2276 recorded catch attempts. A Convolutional Neural Network (CNN) is used for feature extraction with downstream Long Short-Term Memory (LSTM) to classify the video data. Classification of the audio data is performed using a one-dimensional CNN. With the chosen neural network architecture, an accuracy of 92.19% was achieved in detecting whether a pass had been caught or not. The feasibility for automatic classification of catch attempts during automated catch training is confirmed with this result.
Differentially Private Timestamps Publishing in Trajectory

In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have realized the privacy-preserving mechanism of location data in social media in terms of data utility and privacy preservation, but rarely have any of them considered the correlation between timestamps and geographical location. To solve this problem, in this paper, we first propose a k-anonymity-based mechanism to hide the user’s specific time segment during a single day, and then propose an optimized truncated Laplacian mechanism to add noise to each data grid (the frequency of time data) of the anonymized time distribution. The time data after secondary processing are fuzzy and uncertain, which not only protects the privacy of the user’s geographical location from the time dimension but also retains a certain value of data mining. Experiments on real datasets show that the TDP privacy-preserving model has good utility.
Monocular Depth Estimation Using a Laplacian Image Pyramid with Local Planar Guidance Layers

It is important to estimate the exact depth from 2D images, and many studies have been conducted for a long period of time to solve depth estimation problems. Recently, as research on estimating depth from monocular camera images based on deep learning is progressing, research for estimating accurate depths using various techniques is being conducted. However, depth estimation from 2D images has been a problem in predicting the boundary between objects. In this paper, we aim to predict sophisticated depths by emphasizing the precise boundaries between objects. We propose a depth estimation network with encoder-decoder structures using the Laplacian pyramid and local planar guidance method. In the process of upsampling the learned features using the encoder, the purpose of this step is to obtain a clearer depth map by guiding a more sophisticated boundary of an object using the Laplacian pyramid and local planar guidance techniques. We train and test our models with KITTI and NYU Depth V2 datasets. The proposed network constructs a DNN using only convolution and uses the ConvNext networks as a backbone. A trained model shows the performance of the absolute relative error (Abs_rel) 0.054 and root mean square error (RMSE) 2.252 based on the KITTI dataset and absolute relative error (Abs_rel) 0.102 and root mean square error 0.355 based on the NYU Depth V2 dataset. On the state-of-the-art monocular depth estimation, our network performance shows the fifth-best performance based on the KITTI Eigen split and the eighth-best performance based on the NYU Depth V2.