Reviewerly/exHarmony huggingface dataset:
Duality approach to quantum annealing of the 3-variable exclusive-or satisfiability problem (3-XORSAT)

Classical models with complex energy landscapes represent a perspective avenue for the near-term application of quantum simulators. Until now, many theoretical works studied the performance of quantum algorithms for models with a unique ground state. However, when the classical problem is in a so-called clustering phase, the ground state manifold is highly degenerate. As an example, we consider a 3-XORSAT model defined on simple hypergraphs. The degeneracy of classical ground state manifold translates into the emergence of an extensive number of $Z_2$ symmetries, which remain intact even in the presence of a quantum transverse magnetic field. We establish a general duality approach that restricts the quantum problem to a given sector of conserved $Z_2$ charges and use it to study how the outcome of the quantum adiabatic algorithm depends on the hypergraph geometry. We show that the tree hypergraph which corresponds to a classically solvable instance of the 3-XORSAT problem features a constant gap, whereas the closed hypergraph encounters a second-order phase transition with a gap vanishing as a power-law in the problem size. The duality developed in this work provides a practical tool for studies of quantum models with classically degenerate energy manifold and reveals potential connections between glasses and gauge theories.
Technical Debt Guild

Efficient Technical Debt Management (TDM) requires specialized guidance so that decisions taken are oriented to add value to the business. Because it is a complex problem that involves several variables, TDM requires a systemic look that considers professionals' experiences from different specialties. Guilds have been a means technology companies using the Spotify methodology have found to unite specialized professionals around a common interest. This paper presents the experience in implementing a guild to support TDM's source code activities in a software development organization, using the action research method. The project lasted two years, and approximately 100 developers were involved in updating about 63,300 source-code files. The actions resulting from the TDM guild's efforts impacted the company's culture by introducing new development practices and standards. Besides, they positively influenced the quality of the artifact delivered by the developers. This study shows that, as the company acquires maturity in TDM, it increases the need for professionals dedicated to TDM's activities.
Chameleon: Contactless Operation Method for Public Devices

Due to the spread of new coronavirus infections, it is desirable to allow people to use public devices such as PIN type smart locks or vending machines without touching them directly. Therefore, we developed a method that enables contactless operation of various public devices using personal smartphones while reducing the impact on existing public devices. This method imitates chameleons of the animal. Therefore, we call it chameleon. We developed the chameleon based on Web application and applied it to PIN type smart locks. Then, we confirmed the effectiveness of the chameleon as the method of contactless operation.
Fast Traffic Light Recognition Using a Lightweight Attention-Enhanced Model

Traffic light recognition is a research hotspot in the field of intelligent transportation and unmanned driving. However, in the actual scene, the complexity of the surrounding environment increases the difficulty of traffic light detection, resulting in the detection accuracy often cannot meet the actual requirements. In view of the small scale of traffic lights compared with other targets in traffic scenes, a lightweight attention-enhanced model is proposed in this paper. Firstly, a attention-enhanced feature extraction network is proposed to increase the feature description ability of small-scale objects; Secondly, the proposed network structure integrates high-level semantic features and low-level detail features, which not only gives full play to the strong information representation of low-level feature details, but also makes use of high-level semantic information features, which is conducive to the classification of small objects; Thirdly, the refinement anchor strategy makes the network more suitable for detecting traffic lights. We have carried out experimental verification on the open Bosch traffic signal dataset. Experimental results show that our proposed algorithm can dramatically improve the accuracy of traffic signal recognition.
Tutorial: Investigating Advanced Exploits for System Security Assurance

Investigation of existing advanced exploits is crucial for system security assurance. One way to achieve system security assurance is through evaluating defenses using qualitative security metrics and accurate measurement methodologies. Analyzing existing exploit techniques can provide crucial insights about qualitative security metrics and measurement methodologies.In this tutorial, we investigate existing advanced exploit techniques by dividing the exploits into their constituent components. Our analyses focus on the impact of different defense techniques on the individual exploit components. These impact analyses provide insights for finding security metrics/methodologies as well as improving existing defenses. In this tutorial, we aim to focus on Return-Oriented Programming (ROP), Just-In-Time Return-Oriented Programming (JITROP), and Data-Oriented Attacks (DOAs). We aim to cover defenses such as fine-grained Address Space Layout Randomization (ASLR) and pointer protection techniques. More specifically, we aim to quantify the impact of fine-grained ASLR on different components of advanced ROP attacks. Besides, we will demonstrate a data-oriented exploit&#x2013;an attack technique that circumvents currently deployed defenses&#x2013; and explore defense techniques for defending against DOAs.Through this tutorial, we aim to improve people&#x2019;s understanding and awareness of fundamental operating system security. The hands-on portion of the proposed tutorial will empower participants and researchers by providing knowledge on low-level security, application-level defenses, and security metrics/methodologies.
The Bibliometric Analysis the Digital Forensics Researcher in Indonesia Based on Garba Rujukan Digital: 2008–2020

The GARUDA (Garba Rujukan Digital) Portal is a digital research reference platform in Indonesia which is managed by Badan Riset dan Inovasi Nasional, From ministry of Research and Technology. It has sources of research data in Indonesia with various research fields. To perform bibliometric analysis sourced from the GARUDA portal, web scraping techniques can be used. In principle, the data generated from web scraping does not experience problems, it only needs data validation because the information displayed on the GARUDA portal is not consistent. After the data is validated, bibliometrics analysis can be performed. The results of this analysis found that the province of Y ogyakarta Special Region has a digital forensics research ecosystem. From this research, its concluded that 9 out of 10 Indonesia reseachers that focused in digital forensic, come from Special Region of Y ogyakarta Province.
A Convergence Monitoring Method for DNN Training of On-Device Task Adaptation

DNN training has become a major workload in on-device situations to execute various vision tasks with high performance. Accordingly, training architectures accompanying approximate computing have been steadily studied for efficient acceleration. However, most of the works examine their scheme on from-the-scratch training where inaccurate computing is not tolerable. Moreover, previous solutions are mostly provided as an extended version of the inference works, e.g., sparsity/pruning, quantization, dataflow, etc. Therefore, unresolved issues in practical workloads that hinder the total speed of the DNN training process remain still. In this work, with targeting the transfer learning-based task adaptation of the practical on-device training workload, we propose a convergence monitoring method to resolve the redundancy in massive training iterations. By utilizing the network's output value, we detect the training intensity of incoming tasks and monitor the prediction convergence with the given intensity to provide early-exits in the scheduled training iteration. As a result, an accurate approximation over various tasks is performed with minimal overhead. Unlike the sparsity-driven approximation, our method enables runtime optimization and can be easily applicable to off-the-shelf accelerators achieving significant speedup. Evaluation results on various datasets show a geomean of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.2\times$</tex> speedup over baseline and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.8\times$</tex> speedup over the latest convergence-related training method.
Improving Model Stability and Training Efficiency in Fast, High Quality Expressive Voice Conversion System

Voice conversion (VC) systems have made significant progress owing to advanced deep learning methods. Current research is not only concerned with high-quality and fast audio synthesis, but also richer expressiveness. The most popular VC system was constructed from the concatenation of an automatic speech recognition module with a text-to-speech module (ASR-TTS). Yet this system suffers from errors in recognition and pronunciation and it also requires a large amount of data for a pre-trained ASR mode l. We propose an approach to improve the model stability and training efficiency of a VC system. Firstly, a data redundancy reduction method is used to balance the distribution of vocabulary to avoid uncommon words being ignored during the training process; by adding connectionist temporal classification (CTC) loss, the word error rate (WER) of our system reduces to 3.02%, which is 5.63 percentage points lower than that of the ASR-TTS system (8.65%), and the inference speed (e.g., real-time rate 19.32) of our VC system is much higher than that of the baseline system (real-time rate 2.24). Finally, emotional embedding is added to the pre-trained VC system to generate expressive speech conversion. The results show that after fine-tuning on the multi-emotional dataset, the system can achieve high quality and expressive speech synthesis.
A circular layout algorithm for attributed hierarchical graphs with ports

Abstract Information visualization based on graph models is a key component of support tools for many applications in science and engineering. The Visual Graph system is intended for visualization of big amounts of complex information on the basis of attributed hierarchical graph models. In this paper, a circular layout algorithm for attributed hierarchical graphs with ports and its effective implementation in the Visual Graph system are presented.
Introduction

This chapter provides basic machine learning mechanisms. It includes supervised, unsupervised, semisupervised, and reinforcement learning algorithms. Besides, different learning techniques are discussed, namely rule-based learning, feature-based learning, and representational learning. Deep learning fundamental and its importance are discussed in the medical imaging field by taking recent works in the domain. Lastly, the scope of the book is neatly discussed.
A systematic review on how blockchain unlock business values

Blockchain, a revolutionary technology is a buzzword and has gained huge popularity and attention. Blockchain creates an immutable chain of transaction blocks without the intervention of the trusted third party that enables secured trustless environments. This technology can support a wide range of applications from identity management to IoT applications. Based on the extensive review of current literature and research, this article discusses the concept behind blockchain and its applications in business. Examples of organization that provided blockchain platform for their clients are also discussed in this work.
The Trade-off Between Privacy and Utility in Local Differential Privacy

In statistical queries work, such as frequency estimation, the untrusted data collector could as an honest-but-curious (HbC) or malicious adversary to learn true values. Local differential privacy(LDP) protocols have been applied against the untrusted third party in data collecting. Nevertheless, excessive noise of LDP will reduce data utility, thus affecting the results of statistical queries. Therefore, it is significant to research the trade-off between privacy and utility. In this paper, we first measure the privacy loss by observing the maximum posterior confidence of the adversary (data collector). Then, through theoretical analysis and comparison we obtain the most suitable utility measure that is Wasserstein distance. Based on these, we introduce an originality framework for privacy-utility tradeoff framework, finding that this system conforms to the Pareto optimality state and formalizing a payoff function to find optimal equilibrium point under Pareto efficiency. Finally, we illustrate the efficacy of our system model by the Adult dataset from the UCI machine learning repository.
UH-JLS: A Parallel Ultra-High Throughput JPEG-LS Encoding Architecture for Lossless Image Compression

The lossless image compression technique has a great application value in distortion-sensitive applications. JPEG-LS, as a mature lossless compression standard, is widely adopted for its excellent compression ratio. Many hardware JPEG-LS compressors are proposed on FPGAs and ASICs to achieve high energy efficiency and low cost. However, JPEG-LS has a contextual Read-After-Write (RAW) issue, making previous hardware either insufficiently explore its parallelism potential or induce other defects while parallelizing, such as compression ratio dropping and compatibility problems. In this paper, we propose a hardware/software co-design method for high-performance JPEG-LS compressor design. At the software level, we propose a pixel grouping scheduling scheme and the Pseudo-LS method to tap the parallelism aiming at the RAW issue. At the hardware level, we discuss the high-performance design methods of these software-level schemes and propose a design space exploration method to constrain the resource usage introduced by parallelization. To our knowledge, our architecture, UH-JLS, is the first pixel-level parallelization streaming image compressor based on the standard JPEG-LS. The experiments show that in the lossless manner and the Pseudo-LS manner, UH-JLS respectively achieves 5.6x and 7.1x speedup than the previous state-of-the-art FPGA-based JPEG-LS compressor.
Digit Semantics based Optimization for Practical Password Cracking Tools

Users usually create their passwords with meaningful digits, i.e. digit semantics, which can be partially exploited by probabilistic password guessing models with a data-driven methodology for better efficiency. However, these semantics are largely ignored by current practical password cracking tools, like John the Ripper (JtR) and Hashcat.
The application of artificial intelligence in emotion detection

Accurate recognition of micro-expressions facilitates emotional communication, and correct capture of expressions is associated with deception detection. The combination of artificial intelligence and psychology can be widely used in various fields such as national life, agricultural production, product development, and garrison construction. This study analyzes and explores whether there is a correlation between parenting style and micro-expression recognition ability, which can provide a basis for subsequent artificial intelligence to detect emotions. This paper mainly uses offline collection of micro-expression recognition data and filling out questionnaires at the same time to collect data and conduct survey research. A total of 175 questionnaires were collected, of which 175 were valid. 110 data of micro-expression recognition were collected, of which 100 were valid. The results found that there was a significant correlation between the parenting style and micro-expression recognition ability of college students. Specifically, the rejection dimension of parents was significantly and negatively correlated with micro-expression recognition ability. Artificial intelligence has a wide range of fields, among which the combination of machine learning and expression recognition can effectively improve the possibility of screening out suspicious people in specific situations.
Symmetry as a Guide to Post-truth Times: A Response to Lynch

Abstract William Lynch has provided an informed and probing critique of my embrace of the post-truth condition, which he understands correctly as an extension of the normative project of social epistemology. This article roughly tracks the order of Lynch’s paper, beginning with the vexed role of the ‘normative’ in Science and Technology Studies , which originally triggered my version of social epistemology 35 years ago and has been guided by the field’s ‘symmetry principle’. Here the pejorative use of ‘populism’ to mean democracy is highlighted as a failure of symmetry. Finally, after rejecting Lynch’s appeal to a hybrid Marxian–Darwinism, Carl Schmitt and Thomas Hobbes are contrasted en route to what I have called ‘quantum epistemology’.
A Blockchain-Assisted Privacy-Preserving Cloud Computing Method with Multiple Keys

How to analyze users' data without compromising individual privacy is an important issue in cloud computing. In order to protect privacy and enable the cloud to perform computing, users can apply homomorphic encryption schemes to their data. Most of existing homomorphic encryption-based cloud computing methods require that users' data are encrypted with the same key. While in practice, different users may prefer to use different keys. In this paper, we propose a privacy-preserving cloud computing method which adopts a double-trapdoor homomorphic encryption scheme to deal with the multi-key issue. The proposed method uses two cloud servers to analyze users' encrypted data. And we propose to use blockchain to monitor the information exchanged between the servers. Security analysis shows that the introduction of blockchain can help to prevent the two servers from colluding with each other, hence data privacy is further enhanced. And we conduct simulations to demonstrate the feasibility of the propose method.
Energy-aware Coflow Scheduling for Sustainable Workload Management

Handling High-Performance Computing (HPC) workflows often requires the orchestration of a collection of parallel flows. Traditional techniques to optimize flow-level metrics do not perform well in optimizing such collections because the network is usually agnostic to application requirements. A Coflow is a recently proposed abstraction that created new opportunities in network scheduling for datacenter networks. However, recent work on coflow scheduling has focused on merely two objectives: decreasing communication time of data-intensive jobs and guaranteeing predictable communication time. In this paper, we take a step further and propose some initial results towards the design of heuristics that optimize also the energy consumption of a data center that hosts HPC jobs. To this aim, we built and released an energy-aware coflow scheduling simulator to the community that helps analyze the tradeoff between energy efficiency and coflow completion time. We also propose two scheduling algorithms that consider coflow completion time, CPU utilization, and energy consumption efficiency. Our initial results using the simulator clarify how each policy should be tuned to the application needs and the computational resources available.
Blockchain-based smart payment scheme of power infrastructure funds

Nowadays, the construction of smart grids and new digital infrastructure is vigorously promoted by the government, so that the power infrastructure is gradually developing towards intelligence and digitization. However, various challenges such as the industry barrier difficult to remove, the unclear supervision policy, and unmatured technology specifications are brought into the present infrastructure construction. In order to improve the timeliness and accuracy of the power infrastructure fund payment, increase the credibility of data, and enhance the quality and effectiveness of supervision and review, a blockchain-based smart payment scheme for power infrastructure is proposed in this work. First, a smart payment business framework of power infrastructure based on blockchain technology is designed. On this basis, the overall framework of the smart payment scheme and the relationships between participants of the blockchain are analyzed. Then, the smart contract for power infrastructure fund payment based on Remix IDE is customized and the correctness and efficiency of the scheme are verified. Finally, the effectiveness and advantages of the scheme are analyzed from the perspective of reliability, security, and efficiency. The simulation result shows that the smart contract based payment process of power infrastructure funds can be optimized and simplified by the proposed scheme and the overall processing time can be reduced by more than 30%.
e-Control: Electronic Attendance Control System for Multi-Condition Class using Microservices Architecture Development Methods

One of the impacts from Covid-19 is the school &#x0026; lectures must be held online. There are many applications that used to do online lectures as the alternative way of face-to-face lectures such as Zoom and Google Meet. In this multi conditional school lectures (offline &#x0026; online), both of the electronic or manual attendance system caused delay of control. e-Control (digitalized attendance system) is a system for controlling and recording the attendance of students and lecturers in a lecture using architectural microservices development method is made without hardware. This system was created starting from making a prototype design using figma, creating a database using phpmyadmin, followed by making an API gateway and API services on the backend using visual code studio, and creating a frontend system using android studio. The attendance system with the fingerprint-based attendance device on android uses and Google Maps as locator that has 2 functions, named as an online attendance system and an offline attendance system, and has 2 features in it, named absence and history. e-Control works well, because it shows the results of observations in testing data transmission to the server resulting in a data transmission time delay of only 0.344 seconds. The results of making e-Control are intended for students and lecturers whose data has been arranged in the database and fingerprint has been set on the android system. This system is also targeted for students and lecturer, the system has already been set in database and fingerprint in the android system as well.
An Augmented Reality Design Method Based on ARKit for the Fusion of Hierarchical Traditional Media and Real Scenes

Augmented reality (AR) has entered our lives, and it is likely to become the next generation of human-computer interaction technology. In order to bring a more immersive augmented reality experience, this paper introduces an augmented reality method that can integrate traditional media with real scenes based on ARKit. Through scene recognition, plane detection, 3D modeling and other technologies, traditional media is integrated into the real scenes to give users an immersive feeling. In addition, this paper designs a hierarchical augmented reality experience based on the processing capabilities of different devices and scene information, avoiding the impact of device differences and lack of scene information on the augmented reality experience.