Reviewerly/exHarmony huggingface dataset:
File Access Patterns of Distributed Deep Learning Applications

Nowadays, Deep Learning (DL) applications have become a necessary solution for analyzing and making predictions with big data in several areas. However, DL applications introduce heavy input/output (I/O) loads on computer systems. These types of applications, when running on distributed systems or distributed memory parallel systems, handle a large amount of information that must be read in the training stage. Inherently parallel and distributed systems and persistent file accesses can easily overwhelm traditional shared file systems and negatively impact application performance. In this way, the management of these applications constitutes a constant challenge due to their popularity in HPC systems. Scientific applications or simulators have traditionally been executed and are optimized for this type systems. Therefore, it is essential to identify the key factors involved in the I/O of a DL application to find the most appropriate form of configuration to minimize the impact of I/O on the performance of this type of application. In the present work, we present an analysis of the behavior of the patterns generated by I/O operations in the training stage of distributed deep learning applications. We selected two well-known datasets such as CIFAR and MNIST to describe file access patterns.
Quantum Computing Today and Tomorrow

The previous chapters introduced qubits as theoretical objects that can be entangled and used for computation. But how do these objects look like in practice? How are today’s quantum computers constructed and what are qubits made of? It’s similary with quantum computing as with power generation: There are, for example, wind turbines, solar cells, and hydroelectric plants. All of them produce electricity, but in fundamentally different ways. Depending on the location, one type of power generation might prove more efficient. Whether one method will outperform the others in the future or whether we will always rely on a mix of energy sources is hard to tell. Similarly, there are many different approaches to quantum computing. They all exhibit advantages and disadvantages, and might be best suited for different tasks. Therefore, it is hard to predict whether there will be a single or many different types of quantum computers in the future. This chapter gives an insight into current quantum computers, based on ions and superconducting circuits. It also offers an outlook on how future quantum computers could help answer questions relevant to society.
A Survey on Billing Models for Cloud-Native Applications

Recent development in Cloud-Native Applications (CNAs) have stimulated the need to explore novel and applicable billing models. Cloud Service Providers (CSPs) are now beginning to include intervals more granular for pricing and resource allocation management strategie’s, through an approach called micro-billing. Although many CSPs are applying billing techniques in sub-minute ranges, the existing specialized literature does not study micro-billing for CNAs and its correlation with micro-services. This work presents an exploratory research to identify main opportunities on billing models for CNAs through a systematic literature review to contribute as a basis for future research works in the field, mainly related to micro-billing models for CNAs.
Mitigating Vulnerabilities in Closed Source Software

Many techniques have been proposed to harden programs with protection mechanisms to defend against vulnerability exploits. Unfortunately the vast majority of them cannot be applied to closed source software because they require access to program source code. This paper presents our work on automatically hardening binary code with security workarounds, a protection mechanism that prevents vulnerabilities from being triggered by disabling vulnerable code. By working solely with binary code, our approach is applicable to closed source software. To automatically synthesize security workarounds, we develop binary program analysis techniques to identify existing error handling code in binary code, synthesize security workarounds in the form of binary code, and instrument security workarounds into binary programs. We designed and implemented a prototype or our approach for Windows and Linux binary programs. Our evaluation shows that our approach can apply security workarounds to an average of 69.3% of program code and the security workarounds successfully prevents exploits to trigger real-world vulnerabilities.
A treatise on the Rilke cryptogram

The ‘Rilke Cryptogram’ is a possible cipher of German, WWII origin. This study summarises the text’s history, and examines explanations for its nature. Statistical techniques are applied to an updated transcription. The text consists of 18,760 characters in 3,305 unique groups of four, arranged into 670 rows by seven columns on 33 pages of an otherwise ordinary book. The distribution of these groups is not particularly Zipfian, suggesting the text is likely not a code or monoalphabetic substitution cipher. Its alphabet is longer than English and German (46 characters). Character and N-gram entropy analyses suggest the text is less ordered than English and German, but more ordered than random text. Machine-learning modelling suggests the text is a substitution cipher, but frequency and Kasiski-Kerckhoff analyses suggest otherwise. Sukhotin’s algorithm results for the text are somewhat consistent with results for German, though N-gram distributions do not strongly resemble English or German. Statistical analysis of physical typewriter key distances suggest the text’s groupings are highly unlikely to appear at random, and are consistent with intentional ‘lazy’ typing. The text is inconsistent with machine ciphers of its era (e.g., Enigma), and other features are not entirely consistent with non-cipher explanations. Further investigation is required.
Lightweight image super-resolution with multiscale residual attention network

In recent years, various convolutional neural networks have successfully applied to single-image super-resolution task. However, most existing models with deeper or wider networks require heavy computation and memory consumption that restrict them in practice. To solve the above questions, we propose a lightweight multiscale residual attention network, which not merely can extract more detail to improve the quality of the image but also decrease the usage of the parameters. More specifically, a multiscale residual attention block (MRAB) as the basic unit can fully exploit the image features with different sizes of convolutional kernels. Meanwhile, the attention mechanism can be adaptive to recalibrate channel and spatial information of feature mappings. Furthermore, a local information integration module (LFIM) is designed as the network architecture to maximize the use of local information. The LFIM consists of several MRAB and a local skip connection to complement information loss. Our experimental results show that our method is superior to the representative algorithms in performance with fewer parameters and computational overhead. Code is available at https://github.com/xiaotian3/EMRAB.
Open-set low-shot classification leveraging the power of instance discrimination

In search, exploration, and reconnaissance operations of autonomous ground vehicles, an image recognition capability is needed for specifically classifying targeted objects (relevant classes) and at the same time identifying as unknown (irrelevant and unseen) objects that do not belong to any known classes, as opposed to falsely classifying them in one of the relevant classes. This paper integrates an unsupervised learning feature extraction framework based on the Instance Discrimination method with an Open-Set Low-Shot (IDLS) classifier for creating the desired new capability. Unlabeled images from the vehicle’s operating environment are used for training the feature extractor while a modest number (less than 40) images for each relevant class and unlabeled irrelevant images are used for training the Open-Set Low-Shot (OSLS) classifier in a manner that enables recognition of images unseen during training as irrelevant. The value and the accuracy of the new IDLS approach are demonstrated through a thorough comparison with alternative unsupervised and fully supervised methods.
An ASP Framework for Efficient Urban Traffic Optimization

Avoiding congestion and controlling traffic in urban scenarios is becoming nowadays of paramount importance due to the rapid growth of our cities' population and vehicles. The effective control of urban traffic as a means to mitigate congestion can be beneficial in an economic, environmental and health way. In this paper, a framework which allows to efficiently simulate and optimize traffic flow in a large roads' network with hundreds of vehicles is presented. The framework leverages on an Answer Set Programming (ASP) encoding to formally describe the movements of vehicles inside a network. Taking advantage of the ability to specify optimization constraints in ASP and the off-the-shelf solver Clingo, it is then possible to optimize the routes of vehicles inside the network to reduce a range of relevant metrics (e.g., travel times or emissions). Finally, an analysis on real-world traffic data is performed, utilizing the state-of-the-art Urban Mobility Simulator (SUMO) to keep track of the state of the network, test the correctness of the solution and to prove the efficiency and capabilities of the presented solution.
Proceedings 38th International Conference on Logic Programming

In the last years, the Hyper-parameter Optimization (HPO) research field has gained more and more attention.Many works have focused on finding the best combination of the Deep Neural Network's (DNN's) hyper-parameters (HPs) or architecture.The state-of-the-art algorithm in terms of HPO is Bayesian Optimization (BO).This is because it keeps track of past results obtained during the optimization and uses this experience to build a probabilistic model mapping HPs to a probability density of the objective function.BO builds a surrogate probabilistic model of the objective function, finds the HPs values that perform best on the surrogate model and updates it with new results.In this work, a system was developed, called Symbolic DNN-Tuner which logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, therefore improving performance.Symbolic DNN-Tuner improve BO applied to DNN by adding an analysis of the results of the network on training and validation sets.This analysis is performed by exploiting rule-based programming, and in particular by using Probabilistic Logic Programming (PLP).
GA-SCS: Graph-Augmented Source Code Summarization

Automatic source code summarization system aims to generate a valuable natural language description for a program, which can facilitate software development and maintenance, code categorization, and retrieval. However, previous sequence-based research did not consider the long-distance dependence and highly structured characteristics of source code simultaneously. In this article, we present a Transformer-based Graph-Augmented Source Code Summarization (GA-SCS), which can effectively incorporate inherent structural and textual features of source code to generate an effective code description. Specifically, we develop a graph-based structure feature extraction scheme leveraging abstract syntax tree and graph attention networks to mine global syntactic information. And then, to take full advantage of the lexical and syntactic information of code snippets, we extend the original attention to a syntax-informed self-attention mechanism in our encoder. In the training process, we also adopt a reinforcement learning strategy to enhance the readability and informativity of generated code summaries. We utilize the Java dataset and Python dataset to evaluate the performance of different models. Experimental results demonstrate that our GA-SCS model outperforms all competitive methods on BLEU, METEOR, ROUGE, and human evaluations.
APLIKASI E-TICKET BERBASIS ANDROID PADA BUMDES DESA SUMBERMULYO

The development of technology is growing rapidly nowadays, one of them is an mobile system that widely used to facilitate all affairs from all fields that can change people's behavior towards the digital era, so that society is greatly facilitated in all aspects, e-tickets are one of the system developments. information that is currently widely used on various platforms such as smartphones and computers, android is an OS that has many advantages and lots of features to make it easier for us to do various jobs, one of which is selling tickets using android for ticket sales so that ticket sales work more efficiently. today's smartphone is not only a means of communication, but smart phones have many uses, including for business, games, payments, etc. With the development of science and technology, people's lifestyles have been like culture, economy, society, and education, So this research we use android to be able to run the e-ticket application using a Bluetooth printer to make it more efficient place and time for data management which later this application is made with a hybrid mobile application with fremwork7 to connect to our database using API and MySQL.
Polynomial Filtering Algorithms under Quadratic Nonlinearities in System and Measurement Equations: Comparison with Extended and Second-Order Kalman Filters1

The paper considers the filtering problem under quadratic nonlinearities both in system and measurement equations. A polynomial filter, which is a Kalman-type recursive algorithm, is proposed. The similarities between this algorithm and other Kalman-type algorithms such as extended and second-order Kalman filters are discussed. The procedure for estimating the performance and comparing the algorithms is presented. The advantages of proposed algorithm are illustrated using a navigation data processing example
Mobile Robot Autonomous Navigation: A Path Planning Approach

This work proposes a novel approach for solving the problem of safe autonomous navigation and path planning for a four-wheel robot. To formulate and state the optimization problem, we first model the robot kinematics, then associate the objective functions and constraints to the model. The multi-objective optimization problem we formulated is then solved using the Decomposition-Coordination (DC) technique. The DC method consists of subdividing the global complex problem into smaller sub-problems that are considerably easier to solve and can be processed in parallel, which minimizes the computational time and allows the algorithm to generate the optimal path in near real-time. The four-wheel robot starts executing the generated path, except if an obstruction comes across, then it will correct its path with the assistance of an obstacle avoidance algorithm. This same process of obstacle avoidance continues until the robot reaches its desired goal. In this paper, we present a brief proof of the convergence and stability of the DC method. We also explain the principle of the collision avoidance algorithm, and finally, we present the results of our simulations.
PENERAPAN DATA MINING PADA PENERIMAAN MAHASISWA BARU DENGAN ALGORITMA K-MEANS CLUSTERING

This paper aims to grouping data using Clustering method with k-means algorithm to find potential majors and type of schools that produce feature students who have a good GPA score in semester 1 and semester 2 at Politeknik STMI Jakarta. Dataset from academic data for 2017-2020 has been processed with Rapid Miner showing that in Automotive Business Administration study program there are 3 clusters of students where cluster 0 marked as best cluster is dominated by high school students majoring in Science and Social Sciences. Automotive Industry Information System study program produces 2 clusters of students where cluster 0 marked as best cluster is dominated by high school students majoring in science and vocational high school majoring in mechanical engineering. Automotive Industrial Engineering study program produces 2 clusters of students where cluster 1 marked as best cluster is dominated by high school students majoring in science. Polymer Chemical Engineering study program produces 6 student clusters where cluster 4 marked as best cluster which all come from high school students majoring in science.
Logical Bell state measurements with maximum loss-tolerance threshold using linear optics

We show that linear optical logical Bell state measurements—a ubiquitous primitive for quantum information processing—can be realized with theoretically maximum photon loss tolerance (50%), overcoming the previous limit, believed to be at 1 /√ 2.
Noise-Robust and Loss-Tolerant Quantum Steering with Qudits

We introduce a noise-robust and loss-tolerant test of quantum steering designed for single-detector measurements that harnesses the advantages of high-dimensional entanglement. We experimentally demonstrate detection-loophole-free quantum steering under extreme conditions of loss and noise.
Device-Independent Prepare-and-Measure Quantum Key Distribution

We propose a device-independent prepare-and-measure quantum key distribution (DIPQKD) that the transmitter prepares and sends the qubits to the receiver who de-codes by measuring the qubits. Through characterizing the used channel and ruling out any classical strategies of mimicry, one can identify the security of DIPQKD against collective attacks.
Experimental Demonstration of Zero-Photon Subtraction

We demonstrate conditional attenuation of quantum optical states by subtracting zero photons at a variable beam splitter. The attenuation exhibits an interesting dependence on initial photon statistics that mirrors the effects of single-photon subtraction.
Improving Measurement-Device-Independent Quantum Key Distribution in Turbulence Through Dynamic Attenuation

We propose a scheme to tether transmittance fluctuations due to atmospheric turbulence that significantly improves the Hong-Ou-Mandel visibility and secure key rate in free-space measurement-device-independent quantum key distribution under turbulence.
An unconventional ultra-low loss silicon photonics platform for quantum technologies

Our ultra-low-loss silicon photonics platform can play a significant role in the second quantum revolution, supporting not only quantum photonics (e.g., quantum communication) but also solid-state quantum systems (e.g., scaling superconducting quantum computers).
Entanglement between a telecom photon and a spin-Wave solid-state multimode quantum memory

We demonstrate entanglement between a telecom photon and a solid-state multimode quantum memory. The entanglement is maintained for an optical excitation (with a fidelity high enough to violate a Bell inequality) and a spin-wave excitation.
A High-Rate Receiver for Satellite Quantum Key Distribution

We present a high-rate fiber-integrated quantum receiver for phase-encoded satellite-based QKD. We highlight concepts for time synchronization as well as compensation of the Doppler shift and optimized quantum signal processing.
Processing high-dimensional entanglement with a programmable light converter

We demonstrate a reconfigurable and scalable processor of entangled photons based on multi-plane light conversion and perform key tasks of quantum information processing including high-dimensional entanglement certification and random unitary transformations.
Photon scattering rates in trapped ion qubits

We describe a new model for two-photon stimulated Raman transitions and our efforts to test this model in 40 Ca + ions. Compared to previous models, our model predicts lower scattering at certain Raman beam detunings.
Transmission Estimation at the Fundamental Limit with Bright Quantum States of Light

Parameter estimation at the fundamental limit given by the quantum Cramér-Rao bound is an important milestone for quantum metrology. Here, we present experiments that saturate this bound for transmission estimation with a macroscopic quantum state.
Enhancing the Experimental Feasibility of Distributed Quantum Metrology

We present feasible Gaussian schemes for the estimation with Heisenberg-scaling sensitivity of an unknown parameter distributed within an arbitrary optical net-work. We discuss how these schemes overcome some typical experimental drawbacks of quantum-enhanced metrology.
Containerized immutable maritime data sharing utilizing Distributed Ledger Technologies

Abstract In recent years, distributed ledger technologies (DLTs), especially blockchain technology, has become a popular topic and is being implemented in different industries. Ships are expected to be equipped with more advanced equipment and less crew onboard. The communication and information sharing between different maritime stakeholders such as business partners, port authorities, tax authorities, class societies, flag states, IMO and EU become more important than ever. Trust and authenticity are key properties when utilizing data for safety or business critical applications, and will only increase in importance as automation levels increase towards fully autonomous ships and trading systems. The implementation of DLTs as close as possible to the data source helps provide a reliable, trustworthy way to share, process and verify the data. The objective of this paper is to explore how tamperfree maritime data sharing can enable automated transactions between vessels and shore-based entities. Singapores TradeTrust platform has shown that digitalization of international trade has great potential. In this paper we explore how further enhancements may be implemented and how the distributed ledger VeChain, as compared to Ethereum, have some distinct advantages that may be beneficial for continued automation and efficiency gains. The electronic Bill of Lading was taken as an example to demonstrate the concept. Reporting requirements from the other maritime data sharing scenarios are also expected to require a way of ensuring tamperfree data.
Software Engineering

This chapter introduces software engineering as a structured process to develop and design business applications. The chapter deals with both the general phase approach and agile approaches to software development (Extreme Programming (XP) and Scrum). In addition, the basics of object-oriented software development as well as approaches and problems of cost estimation are presented. Since software engineering is carried out as a project, a brief introduction to project management is also included.
White blood cell detection based on improved YOLOv5s

In order to effectively solve the problem of classification and identification of human peripheral blood leukocytes, this paper proposes a classification and identification framework for leukocytes based on an improved YOLOv5 network. YOLOv5s is selected as the network model, and the samples are preprocessed by constructing a priori frame through k-means clustering. Secondly, the original feature map is obtained by the Focus slicing operation, and the original feature map is sent to the Neck network, and the information is transferred and fused through FPN and PAN to obtain the optimal weight. Finally, CIOU_Loss is selected as the loss function of frame regression to achieve high-precision positioning. The experimental result shows the average accuracy of white blood cell detection of the improved YOLOv5s algorithm is 94.8%, which is 10.7% higher than the previous one. It shows that this method has high detection accuracy and can effectively improve the accuracy of human peripheral blood leukocyte identification.