Reviewerly/exHarmony huggingface dataset:
Language Support for Refactorability Decay Prevention

Even code that is free of smells may be at high risk of forming them. In such cases, developers can either perform preventive refactoring in order to reduce this risk, or leave the code as is and perform corrective refactoring as smells emerge. In practice, however, developers usually avoid preventive refactoring during the development phase, and when code smells eventually form, other developers who are less acquainted with the code avoid the more complex corrective refactoring. As a result, a refactoring opportunity is missed, and the quality and maintainability of the code is compromised. In this work, we treat refactoring not as a single atomic action, but rather as a sequence of subactions. We divide the responsibility for these subactions between the original developer of the code, who just prepares the code for refactoring, and a future developer, who may need to carry out the actual refactoring action. To manage this division of responsibility, we introduce a set of annotations along with an annotation processor that prevents software erosion from compromising the ability to perform the refactoring action.
A Model-Driven Generative Self Play-Based Toolchain for Developing Games and Players

Turn-based games such as chess are very popular, but tool-chains tailored for their development process are still rare. In this paper we present a model-driven and generative toolchain aiming to cover the whole development process of rule-based games. In particular, we present a game description language enabling the developer to model the game in a logics-based syntax. An executable game interpreter is generated from the game model and can then act as an environment for reinforcement learning-based self-play training of players. Before the training, the deep neural network can be modeled manually by a deep learning developer or generated using a heuristics estimating the complexity of mapping the state space to the action space. Finally, we present a case study modeling three games and evaluate the language features as well as the player training capabilities of the toolchain.
Language Design Meets Verifying Compilers (Keynote)

The dream of developing compilers that automatically verify whether or not programs meet their specifications remains an ongoing challenge. Such "verifying compilers" are (finally) on the verge of entering mainstream software development. This is partly due to advancements made over the last few decades, but also to the increasingly significant and complex role software plays in the modern world. As computer scientists, we should encourage this transition and help relegate many forms of software error to the history books. One way of increasing adoption is to design languages around these tools which look, on the surface, like regular programming languages. That is, to seamlessly integrate specification and verification and offer something that, for the everyday programmer, appears as nothing more than glorified type checking. This requires, amongst other things, careful consideration as to which language features mesh well with verification, and which do not. The design space here is interesting and subtle, but has been largely overlooked. In this talk, I will attempt to shed light on this murky area by contrasting the choices made in two existing languages: Dafny and Whiley.
Multi-criteria Decision Making and Its Application to Online Learning Platform Selection During the COVID-19 Pandemic Based on TOPSIS Method

The COVID-19 outbreak has made a huge impact across the nation. It really has caused damage on the education system, as governments across the world partially or completely shutting down educational institutions to combat the transmission of the deadly virus. Classroom teachings have been made obsolete by online classes, leading to a rise of web-based learning, which has sure to meet the students to continue their studies. Students, teachers, administrations, and education professionals have experience gathered of significant challenges as a result of the abrupt shift from classroom teaching to web-based learning. As a result, Learning Management Systems (LMS) and Virtual Learning Environments (VLE) have become widely more popular and attractive, especially as a result of the pandemic’s forced educational adjustments. In a web-based learning system, search engines have been found applicable to continue the educational process. Also, the search engines are involved with retrieving necessary documentation (images, text, video, web pages, and so on) from a large database in order to respond to the learner’s request and several online platforms have been made with different features and facilities viz. Zoom, Google Meet, Google Classroom, Microsoft Teams, Cisco Webex, GoTo Meet, skype, etc. In this chapter, we have been applied TOPSIS method for selecting suitable online platform from existing online platforms using Multi-Criteria Decision Making (MCDM) technique. Finally, to illustrate the proposed technique, a realistic scenario for selecting the online platform from a web-based learning system has been exhibited.
Research on pre-trained movie recommendation algorithm based on user behavior sequence

In practical application scenarios, the behavior of users watching movies is random and diverse, and also includes spatiotemporal features. Aiming at the fact that the complex ranking model cannot use a large amount of data for learning and updating in real time, especially the problem of insufficient training data for inactive users, this paper proposes a pre-training-based user embedding algorithm model. In the pre-training stage, the SINE model is used to dig out several intents with the highest user interest, improve the hit rate of user interest, and thus improve the accuracy of Inference. The follow-up test results show that the newly constructed recommendation model has better performance, and the evaluation index AUC is increased by 2.4% compared with the model without pre-training, which proves the effectiveness and feasibility of the new algorithm.
Design of metasurface skin cloak based on a low-complexity monitoring model and deep learning

This work proposes a research scheme to speed up the design of metasurface skin cloak through low-complexity phase monitoring model and deep learning. This skin cloak conceals a three-dimensional arbitrarily shaped object by complete restoration of the phase of the reflected light at specific wavelength. And the possibility of realizing spectral prediction by deep learning is analyzed. During the study, a phase monitoring system was designed in which the detector, the light source and the monitored nano-antenna were sequentially distributed at equal distances from the emitted wavelength of the light source, so that the monitored phase amount was exactly equal to the phase change before the reflected wave, thus eliminating the need for multiple monitors to measure and calculate the phase change before and after the reflection. The traditional metasurface design is usually constructed by manual library construction based on the phase distribution and the relationship between phase variation and dimensional variation of the cell structure, so this work combines the aforementioned monitoring model with deep learning to generate the database required for modeling. The two variable parameters of device length and width were first defined, and the reflected wavefront phase change used as the optical response, and we reprocessed the original data and finally build and trained an artificial neural network model for forward prediction of optical response. This network can obtain its MSE below 0.001 for the test set after the training is completed. Thus the scheme can replace the role of simulation software to some extent, and its prediction process can be completed in a few milliseconds, improving the efficiency of the design metasurface process.
MetaABR

This work focuses on a video bitrate algorithm that quickly adapts to new and various environments with just a few update steps. This aspect is especially important for large-scale video streaming services used by a wide variety of users in different environments. Our proposed model is based on a neural network and employs meta-reinforcement learning to train it. After training, it can be easily customized for a variety of new environments with a few update steps, providing a user-specific streaming service.
Relentless Repairability or Reckless Reuse: Whether or Not to Rebuild a Concern with Your Familiar Tools and Materials

We must retain liveness and exploratory practices within the programming systems that make us feel most productive. However, the temptation to just reuse black boxes through limited interfaces is pervasive. We expect time savings and better performance at the cost of poor repairability. Fortunately, we also know about the benefits of having an open implementation constructed from familiar materials, integrated with familiar tools. Consequently, it is primarily a matter of “just building it” ... again? Piece of cake. What could possibly go wrong?
FLOREnce: A Hybrid Logic-Functional Reactive Programming Language

Functional reactive programming (FRP) languages allow programmers to write declarative expressions to denote event-streams. They feature operators (e.g., map, filter, merge, ...) to build compositions of events. Among these operators we would expect to find the traditional Boolean operators to express complex events. To some extent this is true. However, a remarkable absent in existing FRP languages is the logical conjunction, i.e., the and operator. In this paper, we describe the difficulty of introducing logical conjunction in FRP. We then present a new experimental language called FLOREnce that reconciles functional reactive programming with ‘logic reactive programming’ abstractions. The latter emerge naturally when adding a reactive conjunction operator to an event-based FRP language. We present a prototypical implementation of FLOREnce and we study its expressiveness by conducting an experiment.
The Principles of the Flix Programming Language

We present the design values and design principles of the Flix programming language, a functional-first, imperative, and logic programming language. We explain how these values and principles came into being and how they have influenced the design of Flix over the last several years.
A Hardware Friendly Demosaicking Algorithm based on Edge Sensing

Demosaicking is one of the core modules in the Image Signal Processor (ISP), which converts raw images into RGB images. The demosaicking algorithm is mainly used in ISP, which has high requirements on the complexity and accuracy of the algorithm. For this purpose, we propose a hardware friendly demosaicking algorithm based on edge sensing (EHD). We classify the edges using gradients of color difference and interpolate as far as possible along the edge direction rather than across the edge. The experimental results show that our proposed algorithm achieves high subjective quality and cPSNR of 39.15 dB, which is 0.84 dB better compared to the LED algorithm.
A Fully Parallel On-Die ECC Architecture with High Area Reduction and RAS Enhancement for HBM3

This paper presents an efficient error-correction-code (ECC) scheme which is designed to enhance the Reliability, Availability, and Serviceability (RAS) features and save area for HBM3 DRAM, the latest-generation technology of high bandwidth memory (HBM). In order to improve reliability, a fully parallel Reed-Solomon (RS) encoder/decoder with the double-sub-engine architecture is proposed for the on-die ECC implementation where the symbol-wise interleaver/de-interleaver can provide significantly strong and flexible error correction capabilities. Furthermore, the area of the on-die ECC engine can be vastly reduced by introducing a hardware sharing scheme which can save 35% area overhead as compared to the conventional architecture. The on-die ECC has a symbol size of 8 bits and an ability of double-symbol-correction that can be implemented in future high performance memory systems.
A High-performance Hardware Accelerator using a Fusion Approach of Convolution and Pooling

Convolutional neural networks (CNNs) have shown excellent effect in many fields. However, their increasingly depth and scales make it difficult to apply them in resource constraint scenarios, such as Internet of Things (IOT) and mobile devices. Therefore, by exploiting the data extraction of the pooling layers in CNNs, a Fusion Approach of Convolution and Pooling (FACP) was proposed to eliminate the redundant operations inter convolution and pooling (i.e., average-pooling and max-pooling) layers. In addition, a high-performance and efficient hardware accelerator with an FACP component was designed to deploy our proposed FACP. Our designed accelerator was implemented on the Xilinx XCVU9P platform and run at 300MHz clock frequency. Under the evaluation of AlexNet, our hardware accelerator can achieve 962.45 performance and 0.94 DSP efficiency. Compared with previous works, the performance and DSP efficiency can achieve up to 5.57× and 3.47×, respectively.
High Throughput and Low Latency Hardware of Contrast Limited Adaptive Histogram Equalization Algorithm

Contrast Limited Adaptive Histogram Equalization (CLAHE) is an effective technique to enhance the visibility of details and avoid excessive amplification of noises in a local region of an image. CLAHE statistics the probability density histograms of pixels redistribute the histograms and calculates the output value of pixels by remapping the intensity values using the redistributed histograms. However, high computational complexities and data dependence make the hardware design with high area efficiency and high throughput a difficult problem. In this study, we analyze the dependence of data in the algorithm and present an ideal sequence diagram that describes the relationship between input and output signals. Based on this sequence diagram, flexible line buffers and an efficient memory accessing scheme for histograms are proposed. Besides, we provide a unified interpolation circuit for three cases. As a result, we implement a low latency and high throughput hardware architecture of CLAHE whose performance almost achieves the ideal case as analysis. According to the experimental results, this design operates at up to 133.3 MHz frequency enabling processing 1080p@60fps in real-time on Xilinx XC7Z010 FPGA.
Multi-criteria Decision Analysis and Fuzzy-Decision-Making Trial and Evaluation Laboratory (MCDA and F-DEMATEL) Method for Flipped and Sustainable Mathematics Teaching as a Real-Life Application

Multi-criteria decision analysis (MCDA) method as an instrument has been applied in different fields. Deciding and recognizing the appropriate conditions is important due to adjust the present educational changes, and it is also significant in mathematics education. However, this idea is rarely used in academic discipline. This study tried to examine and categorize proper conditions for mathematics teaching as a real-life utilization with a multi-criteria decision analysis and fuzzy-decision-making trial and evaluation laboratory (MCDA and F-DEMATEL) method. Specifically, adjusting to present educational changes, it is about flipped classroom combined with sustainable development and information, communication, and technology (ICT) in mathematics teaching. In this case, the principal criteria and sub-criteria were carefully chosen after the discussion of an association of specialists as decision-makers with the MCDA and F-DEMATEL methods. Later, the final analyses’ findings were measured with simple additive weighting (SAW) throughout their weight coefficients’ result and susceptibility study with the chosen criteria and sub-criteria. The findings showed that the most significant criteria and sub-criteria for variation to present educational changes had been revealed to be based on the above-cited evaluation methodologies. Thus, this approach could be used to certify the most important flipped and sustainable mathematics teaching for adjusting to present educational changes with resultant learning environments in more long-term education. Hence, it could be related to decision-making defects in private sectors, at government sectors and more educational areas, which continually requires constant actualization of knowledge and distribution.
Towards a Verified Cost Model for Call-by-Push-Value

The λ-calculus is a fundamental model of computation. It provides a foundation for functional programming. Therefore, developing an effective cost model for the λ-calculus is essential for analysing the complexity of functional code. The call-by-push-value λ-calculus allows specifying various evaluation strategies within the same calculus. This enables programmers to specify how they want their program to evaluate within the calculus. There has been ongoing research in developing cost models for the λ-calculus with various evaluation strategies. Our project aims to extend this line of work by providing an effective verified cost model for the call-by-push-value λ-calculus in the HOL4 theorem prover. As a result, it will allow formal verification of the complexity of functional programs.
Ex Libris

Free AccessEx LibrisUlf LiedkeUlf LiedkeSearch for more papers by this authorPublished OnlineDec 2022https://doi.org/10.13109/path.2022.111.12.554SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous article FiguresReferencesRelatedDetails Download book coverVolume 111Issue 12December 2022 ISSN: 0720-6259eISSN: 2197-0831HistoryPublished online:December 2022 PDF download
The vibration knows who you are! A further analysis on usable authentication for smartwatch users

These days, smartwatches are becoming more common and can even operate in stand-alone mode. This increases the need for smartwatches to authenticate users independently without paired smartphones. Currently, password or pattern-based methods can authenticate the smartwatch users in stand-alone mode, but these methods are known to be vulnerable to simple attacks such as shoulder-surfing and password dictionary attacks. In addition, biometric-based methods, which are expected to release on smartwatches in the near future, require inconvenient user interaction or special sensors for measurement. In light of this, we propose a smartwatch user authentication method that does not require any additional sensors or user interaction. Based on the fact that the human body structure affects the way vibrations are absorbed, reflected, and propagated, we designed a smartwatch user authentication method based on a challenge-response structure using vibrations. In our method, a challenge is a set of fresh random vibrations, which are provided by default in current smartwatches, and a response to the challenge is measured by built-in gyroscope and accelerometer sensors. Our earlier study demonstrated that commercial smartwatch users can be authenticated with a low equal error rate (EER) of 1.37 %. In this paper, we extended the analysis of our method on various vibration types by using a prototype setup. As a result, we discovered an outperformed vibration type for user authentication. We conducted further analysis for users with heavier body weights as these individuals are more vulnerable to a not-in-wear attack. Finally, we conducted more advanced impersonation attacks on test participants with one or more similar physical indicators to demonstrate that our method is also secure against a wider range of more complex attacks.
HRPoseFormer: High-Resolution Transformer for Human Pose Estimation via Multi-Scale Token Aggregation

Vision Transformer has a promising application in human pose estimation (HPE). However, due to the use of similar receptive fields of the tokens, existing Transformers are still lacking the ability of dealing with the scale variance. In this paper, we propose a High-Resolution Transformer-based network—HRPoseFormer for HPE. First, we bring in a self-attention module to the parallel multi-resolution structure for a more effective capture of the multi-scale features with lower computing complexity. Second, we utilize detail-specific feed-forward layers to supplement more elaborate local features to the Transformer blocks. Third, we leverage the unbiased data processing (UDP) strategy to help acquire a more accurate transformation between the different coordinate systems and the keypoint formats. Experiments show that the new HRPoseFormer model surpasses the existing state-of-the-art methods with a superior performance of 77.0 AP on the COCO keypoint dataset.
Block-HRG: Block-based differentially private IoT networks release

With the rapid development of the Internet of Things (IoT), privacy-preserving IoT network (graph) analytic has attracted significant attention, where Local differential privacy (LDP) has been widely used. Current LDP-based network publishing approaches commonly require each device to remain online throughout the data collection. Nevertheless, many IoT devices have limited network connectivity, which restricts the applicability of these solutions. Besides, there are extensive collaborations among mutually trusted IoT devices that form natural communities, which is largely ignored by existing approaches, thereby compromising the maintenance of topological utility. To tackle these issues, we introduce block differential privacy, which extends differential privacy to IoT environment. On this basis, we propose a two-phase privacy-preserving IoT network release scheme. By dividing the graph into blocks and applying a hierarchical random graph (HRG)-based structure extraction approach on the inner-block edges, our scheme abandons the attempt to collect data from every single device and therefore can effectively capture the community structure. Furthermore, we propose an HRG aggregation approach that combines the block HRGs and the across-block edges to construct a global graph in a bottom-up manner, which significantly enhances the topological utility. Theoretical analysis and experimental results show that our proposed scheme achieves high-quality IoT graph release while ensuring individual privacy.
Testing the Impact of Vibration on Changes in the Number of Erythrocytes and Platelet Counts in Whole Blood

Blood or plasma is transferred from the unit car to the Blood Donor Unit using a conventional cooler box without any tools to maintain the effect of vibration which is very possible for a decrease in blood quality. Examination carried out to determine the quality of blood through routine hematological examination by counting the number of erythrocytes and platelets. The purpose of this study was to determine the impact of vibration on the number of red blood cells and thrombocytes. The research design was a pre and post test with control group design. The sample size for each group was nine tubes of blood. Data was processed by one way ANOVA and tukeys HSD test. Results There was a significant difference in the number of erythrocytes between the group that was vibrated at a frequency of 11 HZ compared to the group that was vibrated at a frequency of 0 HZ and 5 HZ with p values 0.00 and 0.01, respectively. There was a significant difference in the number of platelets between the groups that were vibrated with a frequency of 11 HZ compared to the groups that were vibrated with a frequency of 0 HZ and 5 HZ with a p value of 0.00. It was concluded that vibrations exceeding 10 Hz could decrease the number of erythrocytes and platelets in whole blood