Reviewerly/exHarmony huggingface dataset:
Fabric Smart Contract Read-After-Write Risk Detection Method Based on Key Methods and Call Chains

Fabric is currently the most popular consortium chain platform with a modular architecture that provides high security, elasticity, flexibility and scalability. Smart contracts realize the automatic execution of transactions and the operation of reconciliation data. The Fabric platform supports general programming languages ​​to write smart contracts. However, in the development process of smart contracts, due to insufficient understanding of the underlying operating logic of smart contracts, developers are prone to introduce some risky operations, resulting in a mismatch between the execution logic of smart contracts and business logic, resulting in a lot of losses. The read-after-write risk is a relatively complex and common security risk in smart contracts. Currently, many detection tools cannot detect this risk. There is an urgent need for a solution that can quickly and accurately detect the read-after-write risk in smart contracts. This paper proposes a static analysis smart contract read-after-write risk detection method based on key methods and call chains. The scheme extracts key method patterns on the abstract syntax tree, identifies and locates key methods with risks, greatly reduces the interference of useless nodes on detection, and realizes rapid detection. By constructing the key method call chain, the real call scene is restored according to the call type and attribute of the key method. After experimental verification, compared with the current popular smart contract risk detection tool Revive^CC, the tool proposed in this paper has higher detection accuracy and can more accurately locate the read-after-write risk in smart contracts.
Subjective Attack Trees

Subjective attack trees (SATs) extend traditional attack trees by taking into account the uncertainty about the probability values of security events. Assigning precise values is often difficult due to lack of knowledge, or insufficient historical data, making the evaluation of risk in existing approaches unreliable, and therefore unreliable security decisions. With SATs, the author seeks to better reflect the reality underpinning the model and offer a better approach to decision-making via the modeling of uncertainty about the probability distributions in the form of subjective opinions, resulting in a model taking second-order uncertainty into account. The author further discusses how to conduct security analysis, such as risk measuring and security investments analysis, under the proposed model. Security investments analysis requires first to incorporate the model with countermeasures and then study how these countermeasures reduce risk in the presence of uncertainty about probability values. The importance and advantage of the SAT model are demonstrated through extended examples.
STUDENTS’ DISCOURSES DURING THE ONLINE DISTANCE LEARNING IN THE FIRST WAVE OF THE COVID19 PANDEMIC: AN ACTION RESEARCH WITH STUDENTS AS CO-RESEARCHERS

The COVID-19 pandemic affected education communities by turning students and teachers abruptly to online teaching. This imposition of digital education is being investigated by various researchers all over the world since it has changed the way we conceive of the use of technology in classrooms and in our lives in general. Nevertheless, the students’ voice is being neglected and not taken seriously into account. In this paper an action research is presented with the teacher acting as researcher and the students as co-researchers. This action research took place during the first wave of the pandemic (March 2020-May 2020) in Crete, Greece. The students, after investigating the online education in general and after various discussions, in their final accounts reflected upon a. the changes in their role as students, b. the changes in the role of the teacher, c. they proposed the role that a teacher should have today. The teacher-researcher analyzed these three categories with Critical Discourse Analysis to identify the discourses the students promoted or silenced. The findings showed that online education is not a success story as promoted by researchers, policymakers, and other stakeholders; rather it lacks human elements like humor, psychological support, and instant interaction.
Balance Control of a Flywheel Inverted Pendulum by Fuzzy Logic Controller

In this study, a flywheel inverted pendulum was modeled as simulation. The model controlled by fuzzy logic and PID controller for comparison. Fuzzy logic controllers were designed using triangular and Gaussian membership functions and various methods that are "and", "implication" and "aggregation". All gains from fuzzy logic controllers and PID were tuned by the trial-and-error method. The best performance was obtained by fuzzy logic controller that uses a triangular membership function and "prob/probor" functions. The results were evaluated in terms of three phenomena. In terms of Settling Time and Maximum Overshoot, Fuzzy Triangle MF with 0.15 s and 0 degrees, respectively, and PID and Fuzzy Triangle MF models with 0 degrees in terms of Steady-State error achieved the best success. In addition, the robustness of the control system was tested by applying two different types of disturbance inputs, random and impulse. The results show that fuzzy logic is a good alternative for balance control of a flywheel inverted pendulum, but PID has an acceptable performance.
Adversarial Transfer Learning

Generative Adversarial Nets (GAN) is one of the most popular research topics in recent years. In this chapter, we introduce adversarial transfer learning methods, which belongs to the implicit feature transformation methods. Specifically, we will introduce GAN-based transfer learning with applications to distribution adaptation and maximum classifier discrepancy. Data generation is another important research topic in adversarial transfer learning. Finally, we present some practice.
Research on Economic Theory Under the Operation of Business Model 4.0

With the emergence of intelligent technology for cyber-physical systems (CPS), manufacturers’ production has come to the age of Industry 4.0, and the corresponding Business Model 4.0 has been born. Under Business Model 4.0, can consumers and producers obtain instant, sufficient, and transparent product or service information through the Internet in mutually beneficial behaviors? This article examines the economic system under the operation of Business Model 4.0 from the perspective of relevant theories of economics and attempt to analyze the theoretical mechanism of the operation of Business Model 4.0. It is found in the article that under the process of Business Model 4.0, it should be possible to quickly and easily achieve the conditions of Pareto efficient allocation in the economic theory. However, everyone involved in the Internet has to behave in favor of others in this Business Model 4.0. If this factor is lacking, the efficiency of the economic system may disappear.
Robustness of Affine and Extended Affine Equivalent Surjective S-Box(es) Against Differential Cryptanalysis

A Feistel Network (FN) based block cipher relies on a Substitution Box (S-Box) for achieving the non-linearity. S-Box is carefully designed to achieve optimal cryptographic security bounds. The research of the last three decades shows that considerable efforts are being made on the mathematical design of an S-Box. To import the exact cryptographic profile of an S-Box, the designer focuses on the Affine Equivalent (AE) or Extended Affine (EA) equivalent S-Box. In this research, we argue that the Robustness of surjective mappings is invariant under AE and not invariant under EA transformation. It is proved that the EA equivalent of a surjective mapping does not necessarily contribute to the Robustness against the Differential Cryptanalysis (DC) in the light of Seberry’s criteria. The generated EA equivalent S-Box(es) of DES and other $$6 \times 4$$ mappings do not show a good robustness profile compared to the original mappings. This article concludes that a careful selection of affine permutation parameters is significant during the design phase to achieve high Robustness against DC and Differential Power Analysis (DPA) attacks.
Graph Signal Processing Over a Probability Space of Shift Operators

Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. However, in many applications, the graph topology may be unknown <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> , its structure uncertain, or generated randomly from a predefined set for each observation. Each graph topology gives rise to a different shift operator. In this paper, we develop a GSP framework over a probability space of shift operators. We develop the corresponding notions of Fourier transform, MFC filters, and band-pass filters, which subsumes classical GSP theory as the special case where the probability space consists of a single shift operator. We show that an MFC filter under this framework is the expectation of random convolution filters in classical GSP, while the notion of bandlimitedness requires additional wiggle room from being simply a fixed point of a band-pass filter. We develop a mechanism that facilitates mapping from one space of shift operators to another, which allows our framework to be applied to a rich set of scenarios. We demonstrate how the theory can be applied by using both synthetic and real datasets.
Swarm intelligence algorithm based on spiking neural-oscillator networks, coupling interactions and search performances

This paper relates to study on a new deterministic particle swarm optimization (D-PSO) called Optimizer based on Spiking Neural-oscillator Networks (OSNNs). OSNNs have a swarm consisting of plural particles which search a solution space interacting with each other. A single particle consists of plural spiking neural oscillators (`spiking oscillators') modeled by integrate-and-fire neurons. The spiking oscillators are coupled by a network topology and interact with each other by exchanging their own spike signals. Such interaction results in that coupling spiking oscillators can take synchronous or asynchronous dynamics and affects search performances of OSNNs. Herein we propose the basic algorithm of OSNNs and applied Ring 1-way network topology to coupling spiking oscillators. We theoretically analyzed parameter conditions for OSNNs, demonstrated the analytic results, and verified search performances of OSNNs through numerical simulations. We also herein discuss search performances of OSNNs and the relationship between the search performances and analytic results, and clarify prospective parameter regions which lead to good search performances in solving optimization problems.
Frequency domain-based bit-depth enhancement method and dealing with different bit-depth degradation

In this paper, we present the effectiveness of a frequency domain-based loss function using the discrete cosine transform (DCT) for the bit-depth enhancement (BDE) problem that recovers high-bit-depth images from low-bit-depth images. By minimizing the loss between the DCT coefficients, it is expected to recover smooth luminance changes by suppressing extra frequency components caused by the weak gradients contained in the false contour artifacts. Moreover, we proposed a frequency domain-based multi-level BDE method to deal with different bit-depth degradation. The proposed multi-level BDE method identifies the bit-depth of the input image by embedding the bit-depth information in the frequency domain, and it recovered the missing lower bits appropriately. Experimental results show that the model optimized with frequency-based loss outperforms the model optimized with other losses in the comparison considering the objective and subjective results. Furthermore, we show that the proposed multi-level BDE method is effective for more severe bit-depth degradation on a specific benchmark dataset.
Application of Interval Type-2 Fuzzy Logic Control Approach to the Lower-Limb Exoskeleton

Fuzzy logic control methods are an integral part of control engineering and are widely used in applications such as robotics, rehabilitation, home appliances, and many more. Here, an interval type-2 fuzzy logic control-based proportional-integral-derivative (IT2-FLC-PID) control method is presented for the lower-limb exoskeleton. The intention of the use of the IT2-FLC-PID approach is to handle the system uncertainties and external disturbances encountered with the exoskeleton plant. The optimal controller parameters can be obtained using a well-known and nature-inspired optimization algorithm, the namely cuckoo search algorithm (CSA). To investigate the efficacy of the IT2-FLC-PID control scheme applied to the exoskeleton system, simulation results are compared with the traditional type-1 FLC-PID (TI-FLC-PID) and the standard PID control approach.
[Paper] Direct Super Resolution for Multiplane Images

A multiplane image (MPI) is a useful 3-D representation composed of a stack of semi-transparent images, from which arbitrary views can be rendered with little computational cost. In this paper, we tackled the problem of super-resolution for MPIs, where a high-resolution MPI is inferred from a lower resolution one. By analyzing the anti-aliasing condition for the light field that would be produced from an MPI, we clarified that such a high-resolution MPI should have smaller sampling intervals over not only the spatial dimension but also the depth dimension. On the basis of this analysis, we constructed a learning-based method to transform a low-resolution MPI into a higher resolution one with depth resolution enhancement. Tested on BasicLFSR dataset, our method achieved 30.54 dB on average, which was 1.29 dB higher than the case without depth resolution enhancement. Visual results indicated that our method can accurately restore high-frequency components. Although super-resolution techniques have been studied extensively for images, videos, and light fields, this is the first work to address the problem of direct super-resolution for MPIs.
Designing Well-Accepted IT Solutions for Emergency Response: Methods and Approaches

This chapter introduces system designers, usability engineers, interaction designers, system analysts, architects, requirements engineers, and project managers to a variety of methods that highly increase both the quality of IT solutions for emergency response and their acceptance among emergency responders. The methods are applicable for solutions at any level of the emergency response hierarchy and for any kind of disaster, but their relevance is highest when the intended solution addresses response frontlines and chaotic, abrupt extreme events such as earthquakes, floods, large-scale accidents, terror attacks, and fires.
A Decision-Support Tool for Experimentation on Zero-Hour Phishing Detection

New, sophisticated phishing campaigns victimize targets in few hours from attack delivery. Some methods, such as visual similarity-based techniques, can spot these zero-hour attacks, at the cost of additional user intervention. However, more research is needed to investigate the trade-off between automatic detection and user intervention. To enable this line of research, we present a phishing detection tool that can be used to instrument scientific research in this direction. The tool can be used for experimentation on assisting user decision-making, evaluating user trust in detection, and keeping track of users’ previous “bad” decisions.
Effective learning algorithm for restricted Boltzmann machines via spatial Monte Carlo integration

Restricted Boltzmann machines (RBMs) are a type of statistical machine learning model used in various applications. However, training RBM models is computationally difficult owing to the requirement of calculating expectations that have a combinatorial explosion. We provide a new and effective learning algorithm based on spatial Monte Carlo integration, which is an extension of the standard Monte Carlo integration and can approximate such intractable expectations with high accuracy. The proposed method exhibited superior performance compared to the standard learning method, i.e., contrastive divergence, in terms of accuracy and learning speed. However, there were cases in which the proposed learning method exhibited reduced performances. Thus, we further demonstrate that a heuristic initialization of the learning parameters can suppress this degradation.
On the Change of Decision Boundary and Loss in Learning with Concept Drift

Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as a quantity to evaluate model performance and trigger drift detection and model updates. Online learning theory mainly focuses on providing generalization bounds for future loss. Usually, these bounds are too loose to be of practical use. Improving them further is not easily possible as they are tight in many cases. In this work, a new theoretical framework focusing on more practical questions is presented: change of training result, optimal models, and ITTE in the presence (and type) of drift. We support our theoretical findings with empirical evidence for several learning algorithms, models, and datasets.
Autonomous Driving Through Road Segmentation Based on Computer Vision Techniques

Autonomous Driving refers to self-driving vehicles without the need of intervention from the human driver. Safety enhancement, energy optimization, comfort, maintenance and cost are the key benefits of Autonomous Driving. Other benefits include - productivity, reduced congestion/traffic, prevention of car crashes, reducing carbon footprint and ease of parking in congested cities as driverless vehicles could drop passengers off and move on. Autonomous Driving can be achieved via processing visual images/videos at runtime and then converting them to vehicle control signals. This study will help in detecting objects on roads (such as moving vehicles, pedestrians, other static objects on road and road segmentation) using deep convolutional neural network (CNN), which in turn help in aiding driverless future. Semantic segmentation can help recognize objects and their location. Semantic segmentation refers to labelling images with pixel-by-pixel classification that in turn helps to perceive the surrounding environment. Human driver also perceives the driving environment in a similar manner. The purpose of this study is to do an overview and check the feasibility of semantic segmentation using deep learning algorithms in the field of Autonomous Driving specifically in road segmentation task. This study will also be comparative study on CNNs models in terms of accuracy, precision, mean IOU and processing time. The scope of this study will include examination and comparison of two popular algorithms - Fully Convolutional Network (FCNs) and Semantic Segmentation model (SegNet). In detail, this study will mainly focus on road segmentation task in Autonomous Driving with the help of CNN models and will conclude which model is best suited for road segmentation task under different weather conditions, in terms of precision, accuracy, mean IoU and processing time.
Flow Control for 5G Smart Distribution Grid Protection

5G communication technology has so many advantages with large bandwidth, high reliability and low latency that the 5G-based differential protection technology becomes a research hotspot in the field of intelligent distribution network protection. However, 5G-based differential protection still have some issues to be addressed, like delay jitter, data security and flow control. On the basis of the analysis of the difference protection technology based on 5G network slices, some methods of controlling flow are provided from multiple aspects like resource allocation, communication protocol, transmission mechanism optimization, algorithm optimization and transmission mode in this paper. (1) It is achievable for flow control to utilize weight-based bandwidth and resource allocation by customizing URLLC slices of 5G network. (2) Compared to TCP, MPTCP is more in line with the requirements of differential protection channel in many aspects, including delay, dislocation control, energy consumption management, security and data volume. (3) The monthly average data flow falls to 1 GB from 6.2208 TB by optimizing the transmission mechanism with the non-standard R-SV protocol of reducing transmission frequency. (4) By optimizing the data calculation algorithm, more than 20 GB of transmitted data will be reduced within one day in normal conditions. (5) Combining 5G with Fiber Channel, 90% traffic can be saved.
Design of Web-Based Agile Meeting Dashboard

In recent days, the agile dashboard plays a vital role in business platforms to organize and manage tasks in an efficient way. Nowadays, communication among team members has become virtual due to COVID-19 pandemic. Considering this situation, the supervisors find it difficult to manage the team. So, a dashboard with the necessary features is needed for a quick glance at the team member’s work and activity. To avoid confusion among the team members while doing a project, what is happening, what is to be done, and what is completely done must be shown in the dashboard. This sets the seal for the team to be motivated and productive in this virtual environment.
Diverse Paraphrasing with Insertion Models for Few-Shot Intent Detection

In contrast to classic autoregressive generation, insertion-based models can predict in a order-free way multiple tokens at a time, which make their generation uniquely controllable: it can be constrained to strictly include an ordered list of tokens. We propose to exploit this feature in a new diverse paraphrasing framework: first, we extract important tokens or keywords in the source sentence; second, we augment them; third, we generate new samples around them by using insertion models. We show that the generated paraphrases are competitive with state of the art autoregressive paraphrasers, not only in diversity but also in quality. We further investigate their potential to create new pseudo-labelled samples for data augmentation, using a meta-learning classification framework, and find equally competitive result. In addition to proving non-autoregressive (NAR) viability for paraphrasing, we contribute our open-source framework as a starting point for further research into controllable NAR generation.