Reviewerly/exHarmony huggingface dataset:
String inference from longest-common-prefix array

The suffix array, perhaps the most important data structure in modern string processing, is often augmented with the longest common prefix (LCP) array which stores the lengths of the longest common prefixes for lexicographically adjacent suffixes of a string. Together the two arrays are roughly equivalent to the suffix tree with the LCP array representing the tree shape. In order to better understand the combinatorics of LCP arrays, we consider the problem of inferring a string from an LCP array, i.e., determining whether a given array of integers is a valid LCP array, and if it is, reconstructing some string or all strings with that LCP array. There are recent studies of inferring a string from a suffix tree shape but using significantly more information (in the form of suffix links) than is available in the LCP array. We provide two main results. (1) We describe two algorithms for inferring strings from an LCP array when we allow a generalized form of LCP array defined for a multiset of cyclic strings: a linear time algorithm for binary alphabet and a general algorithm with polynomial time complexity for a constant alphabet size. (2) We prove that determining whether a given integer array is a valid LCP array is NP-complete when we require more restricted forms of LCP array defined for a single cyclic or non-cyclic string or a multiset of non-cyclic strings. The result holds whether or not the alphabet is restricted to be binary. In combination, the two results show that the generalized form of LCP array for a multiset of cyclic strings is fundamentally different from the other more restricted forms.
PERANCANGAN HELM PROYEK DENGAN METODE INJECTION MOLDING PLASTIK MENGGUNAKAN CAD

Design of a helmet mold project with an injection molding process to provide a simulated picture of a product. The helmet design that will be realized is made of polymer material with an injection molding machine as the main tool. The use of the machine so that time efficiency becomes faster, the mold designed is only in the form of a core and cavity. The software used is Autodesk Inventor 2017 with HDPE (high density polyethylene) material. Making a futuristic helmet product design requires a design style that offers a modern, unique and unusual appearance. The purpose of this design is to be able to design project helmets that are more modern than before. The method used for the design of this project helmet is a simulation of fill analysis, fill time, confidence of fill, quality prediction to show the level of quality of the injection results. The test results of the fill time takes 11.67 seconds, the confidence of the filling capability is 100% filled, and the quality prediction of the product quality is very good 82.1%.
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier

Standard classification methods based on hand-crafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US). In comparison to deep neural networks, commonly perceived as ‘black-box’ models, classical techniques are based on features that have well-understood medical and physical interpretation. However, classifiers based on morphological features commonly underperform in the presence of the shadowing artifact and ill-defined mass borders, while texture based classifiers may fail when the US image is too noisy. Therefore, in practice it would be beneficial to select the classification method based on the appearance of the particular US image. In this work, we develop a deep meta-network that can automatically process input breast mass US images and recommend whether to apply the shape or texture based classifier for the breast mass differentiation. Our preliminary results demonstrate that meta-learning techniques can be used to improve the performance of the standard classifiers based on handcrafted features. With the proposed meta-learning based approach, we achieved the area under the receiver operating characteristic curve of 0.95 and accuracy of 0.91.
A Convolutional Neural Network with Multi-Valued Neurons: a Modified Learning Algorithm and Analysis of Performance

In this paper, some important modifications to a learning algorithm of a convolutional neural network with multivalued neurons (CNNMVN) are introduced. CNNMVN learning is derivative free, and it is based on the generalized error-correction learning rule. Thus, the error backpropagation for this network has its specific features. Modifications, which are introduced in this paper to the error backpropagation, make it possible to improve performance of CNNMVN, speed up its learning process and improve its generalization capability. It is also analyzed which filters are utilized by the convolutional kernels upon completion of the learning process. We also studied which input/output mappings are finally utilized by neurons in a fully connected part of CNNMVN, namely by neurons in its hidden and output layers. We also analyzed how different kinds of pooling affect the learning process and generalization capability of CNNMVN. The simulation results are used to illustrate findings of the paper.
Thread-level Parallelism in Fault Simulation of Deep Neural Networks on Multi-Processor Systems

High-performance fault simulation is one of the essential and preliminary tasks in the process of online and offline testing of machine learning (ML) hardware.Deep neural networks (DNN), as one of the essential parts of ML programs, are widely used in many critical and non-critical applications in Systems-on-Chip and ASIC designs.Through fault simulation for DNNs, by increasing the number of neurons, the fault simulation time increases exponentially.However, the software architecture of neural networks and the lack of dependency between neurons in each inference layer provide significant opportunity for parallelism of the fault simulation time in a multi-processor platform.In this paper, a multi-thread technique for hierarchical fault simulation of neural network is proposed, targeting both permanent and transient faults.During the process of fault simulation the neurons for each inference layer will be distributed among the executing threads.Since in the process of hierarchical fault simulation, the faulty neuron demands proportionally enormous computation comparing to behavioural model of non-faulty neurons, the faulty neuron will be assigned to one thread while the rest of the neurons will be divided among the remaining threads.Experimental results confirm the time efficiency of the proposed fault simulation technique on multiprocessor architectures.
Implementation of an Augmented Reality Application to Improve Learning in Elementary School Children through Playful Games

The use of information technology has changed the way people learn and/or gain knowledge. People with various learning difficulties have greater difficulty in obtaining knowledge and learning. For this reason, the use of technology tools that help to improve this stage of human development is vital, especially for children and people with disabilities. The present research work aims to develop an application that uses augmented reality programming in the Unity development environment to facilitate learning about animals or the animal kingdom as a totality. For this purpose, we followed previously established steps to achieve the objective as well as the distribution of homework. The research was done on the background of the practice or development of this technology in basic education for children between 5 to 9 years old, then the active methodology of gamification for the development of the proposed application ZooAR was carried out to finally compare the differences of our application with the background obtained. It is expected that the development of this technology in different fields of education will be implemented in the future.
Event-Triggered State Estimation Using Particle Filtering Approach

In this paper, we design an event-trigger particle filter (ETPF) for a highly nonlinear process with noisy measurements. The latter are transferred over a wireless network. However, the classical send-on-delta is proposed as a mechanism for reducing the data transmitted from sensors to the remote state estimator. This mechanism helps save bandwidth and energy. It will be shown that by using ETPF the estimation quality can be improved, the effectiveness of the particle filtering approach is demonstrated and compared with Cubature Kalman Filter. Simulation on the two-link robot arm system verify the performance of the proposed approach.
Segmentation of Handwritten Arabic Words Using High Level Informative Scheme

In handwritten Arabic word recognition, the segmentation is a vital and crucial phase, and has a great impact in the accuracy of the subsequent recognition phase. We propose in this paper a new segmentation algorithm based on a high level informative scheme, which describes and identifies different geometric forms in a word, and use them to segment words in characters. The informative scheme we propose gives us the description of skeleton points, their coordinates, natures and number of their neighbors in 8-neighborhood. This allows the extraction of representative primitives of characteristics points that are used in the segmentation phase. The experimental results are promising, and can be improved; we obtained an important number of Good Segmentation Points and a low number of Bad Segmentation Points.
Security Challenges of Blockchain-Based Supply Chain Systems

Blockchain has revolutionized supply chain system security, especially with Internet of Things integration. Deploying blockchain in the supply chain incorporates immutability, transparency, and traceability mechanisms that promote secure data sharing and interactions between stakeholders in trustless environments. A blockchain-based supply chain as a layered architecture consists of three main layers: supply chain, blockchain, and IoT. This type of system is safer and more transparent, with better traceability than traditional supply chain; however, the system faces several security issues. This paper briefly discusses the primary security challenges related to blockchain-based supply chain systems.
Schlieren Visualization of Anisotropic Dual Slanted Plate Mesoscale Lens Action for Ultrasound

Acoustic and photonic hooks have inspired study of new subwavelength structures to be used as lenses for ultrasound. A new V-shaped plate lens structure exhibits anisotropic focusing behavior in 2D finite element method simulations. The near field is shown in both orientations of the structure, and a frequency sweep was performed to study the response of the lens. Such a lens was built from evenly spaced 200 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\mu}\mathbf{m}$</tex> thick brass plates, and the near field was visualized with the schlieren method. The results are compared with the simulation, and anisotropic behavior is confirmed.
Triaging subjects with palpable breast masses for biopsy, follow-up or treatment using AI applied to breast ultrasound in a low-resource setting - A feasibility study

Breast cancer is more common in premenopausal women in developing countries. In the absence of screening, women present with symptoms, usually a palpable mass; however most palpable masses are benign. Automatic triaging of palpable breast masses using ultrasound and Artificial Intelligence (AI) in a low-resource setting would be beneficial by characterizing them (benign, suspicious, malignant) for follow-up with further imaging (suspicious) or biopsy (malignant). Further, automatic prediction of estrogen-receptor-positive (ER+) status of malignant lesions from ultrasound could allow initiation of primary endocrine therapy when appropriate and accelerate the need for specialized immunohistochemistry (IHC), if the tumor appears more likely to be a triple receptor negative, which is more aggressive and faster growing. In this work, we explore methods using Artificial Intelligence (AI) for automatic classification of breast lesions as benign, suspicious and malignant, and prediction of ER+ status for malignant lesions.
Quantum Approximate Optimization Algorithm-Enabled DER Disturbance Analysis of Networked Microgrids

The Quantum Approximate Optimization Algorithm (QAOA) is applied to Networked Microgrids (NMs) in this paper to search for the maximum power exchange section, which is playing an essential role in operating NMs. Mathematically, obtaining the maximum power section is to solve a Max-Cut problem over the modeling graph of NMs. Considering the integration and fluctuations of Distributed Energy Resources (DERs), the maximum power section will change frequently. To efficiently get the section, QAOA provides a powerful solution by leveraging quantum resources. The performance of QAOA highly depends on the critical parameters of quantum circuits. We find the designed parameters for QAOA are still effective under wide range change of output power of energy resources. Tests on a typical NMs system verify the effectiveness of the QAOA method in efficiently searching for the maximum power sections of NMs.
Information acquisition and cognitive processes during strategic decision-making: Combining a policy-capturing study with eye-tracking data

Policy-capturing (PC) methodologies have been employed to study decision-making, and to assess how decision-makers use available information when asked to evaluate hypothetical situations. An important assumption of the PC techniques is that respondents develop cognitive models to help them efficiently process the many information cues provided while reviewing a large number of decision scenarios. With this study, we seek to analyze the process of answering a PC study. We do this by investigating the information acquisition and the cognitive processes behind policy-capturing, building on cognitive and attention research and exploiting the tools of eye-tracking. Additionally, we investigate the role of experience in mediating the relationship between the information processed and judgments in order to determine how the cognitive models of student samples differ from those of professionals. We find evidence of increasing efficiency as a function of practice when respondents undergo the PC experiment. We also detect a selective process on information acquisition; such selection is consistent with the respondents' evaluation. While some differences are found in the information processing among the split sample of students and professionals, remarkable similarities are detected. Our study adds confidence to the assumption that respondents build cognitive models to handle the large amounts of information presented in PC experiments, and the defection of such models is not substantially affected by the applied sample.
Expressions in Java: Essential, Prevalent, Neglected?

Expressions are the building blocks of formal languages such as lambda calculus as well as of programming languages that are closely modeled after it. Although expressions are also an important part of programs in languages like Java, that are not primarily functional, teaching practices typically don't focus as much on expressions.
BatakJava: An Object-Oriented Programming Language with Versions

Programming with versions is a recent proposal that supports multiple versions of software components in a program. Though it would provide greater freedom for the programmer, the concept is only realized as a simple core calculus, called λVL, where a value consists of λ-terms with multiple versions. We explore a design space of programming with versions in the presence of data structures and module systems, and propose BatakJava, an object-oriented programming language in which multiple versions of a class can be used in a program. This paper presents BatakJava’s language design, its core semantics with subject reduction, an implementation as a source-to-Java translator, and a case study to understand how we can exploit multiple versions in BatakJava for developing an application program with an evolving library.
Property Probes: Source Code Based Exploration of Program Analysis Results

We present property probes, a mechanism for helping a developer interactively explore partial program analysis results in terms of the source program, and as the program is edited. A node locator data structure is introduced that maps between source code spans and program representation nodes, and that helps identify probed nodes in a robust way, after modifications to the source code. We have developed a client-server based tool supporting property probes, and argue that it is very helpful in debugging and understanding program analyses. We have evaluated our tool on several languages and analyses, including a full Java compiler and a tool for intraprocedural dataflow analysis. Our performance results show that the probe overhead is negligible even when analyzing large projects.
Proving Memory Access Violations in Isabelle/HOL

Security-critical applications often rely on memory isolation mechanisms to ensure integrity of critical data (e.g., keys) and program instructions (e.g., implementing an attestation protocol). These include software-based security microvisor (SµV) or hardware-based (e.g., TrustLite or SMART). Here, we must guarantee that none of the assembly-level instructions corresponding to a program violate the imposed memory access restrictions. We demonstrate our approach on two architectures (SµV and TrustLite) on which remote attestation protocols are implemented. We extend an approach based on the Binary Analysis Platform (BAP) to generate compiled assembly for a given C program, which is translated to an assembly intermediate language (BIL) and ultimately to Isabelle/HOL theories. In our extension, we develop an adversary model and define conformance predicates imposed by an architecture. We generate a set of programs covering all possible cases in which an assembly-level instruction attempts to violate at least one of the conformance predicates. This shows that the memory access restriction of both SµV and TrustLite are dynamically maintained. Moreover, we introduce conformance predicates for assembly-level instructions that can change the control flow, which improve TrustLite’s memory protection unit.
Model-Driven IoT App Stores: Deploying Customizable Software Products to Heterogeneous Devices

Internet of Things (IoT) devices and the software they execute are often strongly coupled with vendors preinstalling their software at the factory. Future IoT applications are expected to be distributed via app stores. A strong coupling between hard- and software hinders the rise of such app stores. Existing model-driven approaches for developing IoT applications focus largely on the behavior specification and message exchange but generate code that targets a specific set of devices. By raising the level of abstraction, models can be utilized to decouple hard- and software and adapt to various infrastructures. We present a concept for a model-driven app store that decouples hardware and software development of product lines of IoT applications.
Burstiness in data center topologies

Data center workloads are bursty but the impact of the underlying network topology on traffic burstiness is relatively unknown. We show that the symmetric nature of pervasive Clos topologies contributes to traffic burstiness. This is primarily due to a large number of paths of identical size that results in synchronous arrival to hotspots under common traffic patterns such as incast. We also show that some proposed asymmetric data center topologies, such as expanders, may help reduce traffic burstiness due to the larger diversity of their path lengths compared to Clos for workloads that contain large flows. However, for real-world data center traffic with mostly small flows, Clos networks outperform the asymmetric topologies. This is because short flows mostly traverse only the shortest paths and cannot leverage the path length diversity that the underlying topology offers. This shows that leveraging path length diversity requires proactively balancing the load across diverse paths.
Specializing Scope Graph Resolution Queries

To warrant programmer productivity, type checker results should be correct and available quickly. Correctness can be provided when a type checker implementation corresponds to a declarative type system specification. Statix is a type system specification language which achieves this by automatically deriving type checker implementations from declarative typing rules. A key feature of Statix is that it uses scope graphs for declarative specification of name resolution. However, compared to hand-written type checkers, type checkers derived from Statix specifications have sub-optimal run time performance.
Exploring Task Equivalence for Software Engineering Practice Adaptation and Replacement

Practice adaptation in the domain of software engineering (SE) is ubiquitous and well-studied. The main rationale for tailoring lies in the complexity of the environments within which SE projects are implemented. This complexity means that the practices that make up software methodologies are often not fit-for-purpose as-is and must be replaced or adapted to suit local context. However, there is a risk that a practice may be changed in an inappropriate way, with unintended side-effects. For example, altering a practice to reduce documentation may result in a failure to meet standards expectations or the expectations of downstream practices. In this paper, we describe a study that explored the notion of practice equivalence. Our goal was to identify aspects of a practice that are ‘similar’ and might support adaptation. We found that adapted practices must be within the same software function, perform the same action, be compatible in terms of creativity, reasoning and perspectives and have a compatible interface type. Our contributions are the consideration of an as-yet unexplored area of software process that we hope will generate discussion and future research studies and the creation of a strawman framework that will be a starting point for further exploration and evaluation.
Bringing Together Configuration Research: Towards a Common Ground

Configurable software makes up most of the software in use today. Configurability, i.e., the ability of software to be customized without additional programming, is pervasive, and due to the criticality of problems caused by misconfiguration, it has been an active topic researched by investigators in multiple, diverse areas. This broad reach of configurability means that much of the literature and latest results are dispersed, and researchers may not be collaborating or be aware of similar problems and solutions in other domains. We argue that this lack of a common ground leads to a missed opportunity for synergy between research domains and the synthesis of efforts to tackle configurability problems. In short, configurability cuts across software as a whole and needs to be treated as a first class programming element. To provide a foundation for addressing these concerns we make suggestions on how to bring the communities together and propose a common model of configurability and a platform, ACCORD, to facilitate collaboration among researchers and practitioners.