Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepWukong

Static bug detection has shown its effectiveness in detecting well-defined memory errors, e.g., memory leaks, buffer overflows, and null dereference. However, modern software systems have a wide variety of vulnerabilities. These vulnerabilities are extremely complicated with sophisticated programming logic, and these bugs are often caused by different bad programming practices, challenging existing bug detection solutions. It is hard and labor-intensive to develop precise and efficient static analysis solutions for different types of vulnerabilities, particularly for those that may not have a clear specification as the traditional well-defined vulnerabilities. This article presents DeepWukong, a new deep-learning-based embedding approach to static detection of software vulnerabilities for C/C++ programs. Our approach makes a new attempt by leveraging advanced recent graph neural networks to embed code fragments in a compact and low-dimensional representation, producing a new code representation that preserves high-level programming logic (in the form of control- and data-flows) together with the natural language information of a program. Our evaluation studies the top 10 most common C/C++ vulnerabilities during the past 3 years. We have conducted our experiments using 105,428 real-world programs by comparing our approach with four well-known traditional static vulnerability detectors and three state-of-the-art deep-learning-based approaches. The experimental results demonstrate the effectiveness of our research and have shed light on the promising direction of combining program analysis with deep learning techniques to address the general static code analysis challenges.
Conditional Generative Adversarial for in-Situ Layerwise AM

Conditional generative adversarial networks (CGANs) learn a mapping from conditional input to observed image and perform tasks in image generation, manipulation and translation. In-situ monitoring uses sensors to obtain real-time information of additive manufacturing (AM) processes that relate to process stability and part quality. Understanding the correlations between process inputs and in-situ process signatures through machine learning can enable experimental-driven predictions of future process inputs. In this research, in-situ data obtained during a metallic powder bed fusion AM process is mapped with a CGAN. A single build of two turbine blades is monitored using EOSTATE Exposure OT, a near-infrared optical tomography system of the EOS M290 system. Layerwise images generated from the in-situ monitoring system were paired with a conditional image that labeled the specimen cross-section, laser-scan stripe overlap and z-distance to part surfaces. A CGAN was trained using the turbine blade data set and employed to generate new in-situ layerwise images for unseen conditional inputs.
FogBus2: a lightweight and distributed container-based framework for integration of IoT-enabled systems with edge and cloud computing

Edge/Fog computing is a novel computing paradigm that provides resource-limited Internet of Things (IoT) devices with scalable computing and storage resources. Compared to cloud computing, edge/fog servers have fewer resources, but they can be accessed with higher bandwidth and less communication latency. Thus, integrating edge/fog and cloud infrastructures can support the execution of diverse latency-sensitive and computation-intensive IoT applications. Although some frameworks attempt to provide such integration, there are still several challenges to be addressed, such as dynamic scheduling of different IoT applications, scalability mechanisms, multi-platform support, and supporting different interaction models. To overcome these challenges, we propose a lightweight and distributed container-based framework, called FogBus2. It provides a mechanism for scheduling heterogeneous IoT applications and implements several scheduling policies. Also, it proposes an optimized genetic algorithm to obtain fast convergence to well-suited solutions. Besides, it offers a scalability mechanism to ensure efficient responsiveness when either the number of IoT devices increases or the resources become overburdened. Also, the dynamic resource discovery mechanism of FogBus2 assists new entities to quickly join the system. We have also developed two IoT applications, called Conway's Game of Life and Video Optical Character Recognition to demonstrate the effectiveness of FogBus2 for handling real-time and non-real-time IoT applications. Experimental results show FogBus2's scheduling policy improves the response time of IoT applications by 53% compared to other policies. Also, the scalability mechanism can reduce up to 48% of the queuing waiting time compared to frameworks that do not support scalability.
Chaotic Deep Network for Mobile D2D Communication

Device-to-Device (D2D) communication has now become one of the most promising technologies in wireless communications of the Internet of Things (IoT). In view of the requirements on data security, response speed, signal decryption quality and storage cost of mobile devices in D2D networks, we propose chaotic deep network (CDN) to achieve secure transmission which is swifter, higher quality and lower cost. The proposed scheme consists of a prepositive nonrepetitive training procedure, a well-designed parallel encryption process, and a set of pretrained chaotic deep neural decryption networks. Benefiting from the utilization of deep learning methods, CDN achieves the swift and accurate decryption at a much lower sampling rate, which brings huge dimension reduction of both measurement matrices and ciphertext signals. Also, chaotic initial values and parameters are applied to matrix generation and network training, leading to great time reduction, storage saving and security improvement. In addition, CDN incorporates the frameworks of semitensor product (STP) and block-based image processing (BIP), which not only breaks through the dimension matching limitation of matrix multiplication by using STP but also maintains the parallelizable block-cipher mode of BIP. Proved by experiments, CDN at the sampling ratio of 25% achieves almost the same or even better peak signal to noise ratio compared to other 8 most frequently used methods at that of 50% for the same test images, and obtains better visual effects. When using a large-sized image of $2^{10}\times 2^{10}$ pixels for the experiments, CDN is dozens and even hundreds of times faster than those methods, while reduces the size of measurement matrix from a 500-kB level to a 3-kB level, and the size of compressed data to be transmitted can be reduced by more than 50% as well. Besides, the total key space is approximately $10^{93}$ . The adjacent pixel correlation is less than 0.01.
Music Genre Classification using Transfer Learning on log-based MEL Spectrogram

Deep Learning, a branch of Machine Learning is a rapidly expanding field in the Industry 4.0 revolution. The number of applications of Deep Learning are enormous – finding multiple uses in a single domain. Deep Learning enhances current research and provides better perception to a wide spectrum of domains, including Music Information Retrieval. Music is an art, that is widely celebrated worldwide, with countless songs being released or published every day. Music can be classified into many "genres". A statistical study conducted in 2018 shows that there are more than 10 genres of music, with ‘Hip-Hop/Rap ’being the most with most music composed. When one decides to listen to music, one usually has a particular genre in mind, and expects the music application he or she is using to provide numerous songs falling in that genre. A good music application would not only provide many songs, but also increase the ease of access to a particular song or genre. Due to tremendous increase in the amount of digital data available on the internet, the task of accurately classifying music files has become a major problem for music applications like YouTube Music, iTunes or Spotify, which can be solved with the help of Deep Learning techniques. This paper includes an indepth comparison of four transfer learning architectures, i.e. Resnet34, Resnet50, VGG16 and AlexNet, which were the best performing models during different times in ImageNet, to accomplish the task of classification of different songs on the basis of their genre.
Cognitive Load/flow and Performance in Virtual Reality Simulation Training of Laparoscopic Surgery

VR based laparoscopic surgical simulators (VRLS) are increasingly popular in training surgeons. However, they are validated by subjective methods in most research. In this paper, we resort to physiological approaches to objectively research quantitative influence and performance analysis of VRLS training system. The results show that the VRLS could highly improve medical students’ performance (p < 0.01) and enable the participants to obtain flow experience with a lower cognitive load. The performance of participants is negatively correlated with cognitive load through quantitatively physiological analysis.
[DC] Psychophysical Effects of Augmented Reality Experiences

My Ph.D. research aims to explore to which extent Augmented Reality (AR) experiences can evoke measurable physiological, psychological, and neurological responses in the user's body. During the first year of my Ph.D. studies, we have developed an AR experience that enables users to see and hear their own hands burning while looking through a Video See-Through Head-Mounted Display (VST-HMD). In a pilot study, half of the participants reported a heat-sensation on the affected left hand. All participants experienced a significant increase in skin conductance during the experiment. Moreover, participants who experienced a heat sensation had a higher skin conductance response. In a follow up study, we could show that this experience also influences the micro-circulatory blood flow of the hand on some participants.Our research continues on this with several planned follow-up experiments, to explore more deeply how AR experience can have measurable effects on the user's body and mental state. This allows deeper insights into the perceptual and cognitive effects unique to AR experiences. Insights from our experiment may be of significance in a neuroscientific or medical context.
Deriving Digital Surface Models from Geocoded SAR Images and Back-Projection Tomography

Digital surface models (DSMs) are sets of elevation data of the Earth's surface, useful for applications such as urban studies and height estimation of buildings. They can be derived from a set of synthetic aperture radar (SAR) images acquired in an interferometric or tomographic configuration. Each image acquisition is usually focused in radar geometry. In this work, we present steps required to derive DSMs from SAR single-look complex (SLC) products focused in map geometry (geocoded). We modified existing tomographic reconstruction techniques to be able to operate with geocoded SLCs and extended methods to operate with 3-D geocoded SLCs. The performance analysis showed that methods using 3-D geocoded SLC products yielded DSMs with fewer outliers and retained more information of the illuminated area, with a cost of higher computational complexity. Compressive sensing methods using 2-D geocoded SLCs can be a good alternative due to their comparatively moderate computational complexity.
Review of Vedic Multiplier Using Various Full Adders

The complexity of the chip is increasing as the advances in VLSI technology leads to the accumulation of more and more devices on a single chip. Since the chip has high density, the power dissipated in the chip or the amount of the heat produced is also increased resulting in the low power CMOS VLSI designs. In digital computer systems and digital signal processors, multiplication is one of the basic reckoning processes. This paper reviews the 2 by 2 vedic multiplier design using various high speed and low power adders.
Crime Analysis of spatial-temporal distribution based on KNN Algorithm

With the rapid advance of global urbanization, the problem of urban crime is becoming more and more serious, which brings a great challenge to the police all over the world. How to use big data to drive police work has become the hot and difficult point of crime research. In this paper, the research object is theft, battery, narcotics and criminal damage in Chicago. The research method is to use visualization technology and machine learning algorithm to predict the spatial distribution of crime. Firstly, the spatial distribution characteristics of crime occurrence are analyzed by the methods of neighborhood repetition and spatial analysis. Then, the spatial distribution map of aggregated data and the crime distribution heat map are visualized. Finally, we combine the theory of crime distribution to further analyze the spatial-temporal distribution features of several crimes and criminal symbiosis.
Business Analytics: A case-study approach using LDA topic modelling

Strategic decision-making in business is improved through business analytics, which can be descriptive, diagnostic, predictive or prescriptive. Business Analytics uses statistical methods on historic data to gain new perceptions for better policymaking. Topic modelling is a statistical, text-mining, unsupervised machine learning model, that can decipher themes from a corpus such as social media posts, annual reports, social media posts, news covers, related articles, trends in the domain, etc., In this research, Topic modelling is applied to Business Digital Economy Dataset which hosts around 2400 titles, abstracts, keywords from different authors related to various topics on digital economy. Topic modelling has various approaches of which, Latent Dirichlet Allocation (LDA) is most widely used. This paper explores the research articles related to emerging trends in business economy to extract the concealed semantic structures and generate word clouds. The research procedure comprises, introduction of dataset, data pre-processing, building and visualizing the model.
Quantitative evaluation model of desensitization algorithm

Today, with the explosive growth of data volume, data has become an increasingly important strategic resource for various enterprises, but big data not only facilitates human society, but also brings huge privacy leakage problem. Because the data in business production often contains a large amount of user sensitive privacy information, in order to avoid the privacy leakage of these data in the test system and other environments, enterprises begin to pay more attention to desensitization technology. Because desensitization is mainly used in the process of software development in the business field, and is often used to generate internal test data, most of the results are classified or protected by patents. In addition, the evaluation of desensitization algorithm mostly stays at the level of general and qualitative evaluation. At present, there is no intuitive, accurate and universal quantitative evaluation standard. In this paper, aiming at the pain point of desensitization evaluation model, a data oriented quantitative desensitization evaluation model based on association rule mining method is designed. The model does not need to understand the desensitization algorithm design process, but only faces the data set before and after desensitization, which reduces the risk of desensitization algorithm leakage. In addition, the model also covers the common fields of various industries, and accurately and intuitively describes the availability of desensitization data in the subsequent test environment in the form of quantitative numbers, which provides an innovative idea for the unification of desensitization algorithm evaluation standards.
Coverage Modeling and Analysis for Outdoor THz Networks With Blockage and Molecular Absorption

Terahertz (THz) communication has been envisioned as a key technology for providing ultra-wide bandwidth and high-speed data transmission in future networks. However, THz band signal suffers from frequency-dependent path-loss, molecular absorption and line-of-sight (LoS) blockage. Thus, coverage modelling for THz networks has essential difference from low-band networks. In this letter, considering blockage, directional antennas and molecular absorption, we develop an analytical model for coverage probability analysis in outdoor THz networks based on stochastic geometry. Accuracy of the proposed model is validated through Monte-Carlo simulations. Based on the proposed model, we further uncover the impact of nodes density, transmission distance and atmosphere on network coverage and available transmission bandwidth. Numerical results reveal that denser nodes, longer transmission distance, lower latitude and more humid seasons result in lower coverage probability and narrower transmission windows.
User study of an AR reading aid system to promote deep reading

We proposed a reading aid system combining AR HMD and occlusion technology to cultivate students’ intensive reading habits and promote deep reading. A user study was conducted to explore the differences in reading comprehension and subjective evaluation between AR and VR mode. Analysis results show that there are statistically significant differences for realism factors in subjective evaluation between the two modes. The participants confirmed the usefulness of the AR system and preferred the setting of actual paper reading. These results indicate there is potential for AR reading aid system to be used as an educational aid to promote deep reading.
Study of Reconfigurable Intelligent Surface Assisted Communications over Frequency Selected Channels

In this paper, we study a downlink multi-input single-output (MISO) system with the assist of reconfigurable intelligent surfaces (RIS) in the frequency selected channel environment. The transmission from a multi-antenna base station (BS) to the single antenna user equipment (UE) is realized via the multiple propagation paths among which one path is contributed by the RIS reflection. In addition, a practical restriction is considered where only a limited number of discrete phase shifts per RIS element can be provided corresponding to the limited control information from RIS controller. An optimizing problem aiming to maximize the achiveable bit rate is formulated jointly considering the continuous digital beamforming at BS and the discrete phase analog beamforming at RIS. With the help of the spatial propagation model between BS/UE and RIS, the approximate solutions are derived to solve this problem for the case of single subcarrier and multiple subcarriers. The simulation results valify the effectiveness of the proposed approximate solutions for the multiple subcarriers scheduling conditions, and provide a considerable performance gain of RIS assisted MISO system compared to that of the traditional wireless system without RIS assistance.
A Survey on: Personality Prediction from Multimedia through Machine Learning

The prediction of the personality of an individual is a critical problem in both areas whether it is considered in the context of organizations or in the case of our daily lives. Prediction of personality depends on many factors and these factors may vary from one individual to another.Personality prediction is identifying the personalities of individuals through their actions in different situations and observing their behaviours in various circumstances. Personality traits show the different characteristics of different people based on their thoughts, feelings, and behaviours. There can be positive as well as negative personality traits. Personality traits are based on the Big Five Model also known as the OCEAN model i.e. Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. In the previous study, many investigations has been done. They have used different techniques and different algorithms to predict the personality of different people. Some have used handwriting to predict personality using the GSC algorithm. Facial expressions have been used in some studies using CNN features. Few studies have focused on the social networking sites for personality prediction by examining an individual’s reaction to different posts, their comments, their posts, etc. One study predicted personality using AU, LF, POS, Emotional features and their combinations. Apart from this, there are few limitations in these single models discussed above. They work efficiently for only a small dataset but on increasing the size of the dataset their accuracy keeps decreasing. Multimodal is effective in this case and to make the task automatic, an intelligent multimodal agent can identify personality traits better based on both verbal and non-verbal features.
FogFL: Fog-Assisted Federated Learning for Resource-Constrained IoT Devices

In this article, we propose a fog-enabled federated learning framework—FogFL—to facilitate distributed learning for delay-sensitive applications in resource-constrained IoT environments. While federated learning (FL) is a popular distributed learning approach, it suffers from communication overheads and high computational requirements. Moreover, global aggregation in FL relies on a centralized server, prone to malicious attacks, resulting in inefficient training models. We address these issues by introducing geospatially placed fog nodes into the FL framework as local aggregators. These fog nodes are responsible for defined demographics, which help share location-based information for applications with similar environments. Furthermore, we formulate a greedy heuristic approach for selecting an optimal fog node for assuming a global aggregator’s role at each round of communication between the edge and cloud, thereby reducing the dependence on the execution at the centralized server. Fog nodes in the FogFL framework reduce communication latency and energy consumption of resource-constrained edge devices without affecting the global model’s convergence rate, thereby increasing the system’s reliability. Extensive deployment and experimental results corroborate that, in addition to a decrease in global aggregation rounds, FogFL reduces energy consumption and communication latency by 92% and 85%, respectively, as compared to state of the art.
AP2I: Adaptive PII Scanning and Consent Discovery System

Currently, there are several off-the-shelf Personal Identifiable Information (PII) scanning tools available as an assistive tool to find the PII in the network and system endpoints such as server, personal computer, devices, or cloud storage. Most tools have been designed to support General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPPA), California Consumer Privacy Act (CCPA) etc. In Thailand, the Personal Data Protection Act (PDPA) will be enforced in May 2021. Organizations in Thailand are thus now going to be faced with more challenges in relation to security and privacy than they have ever had before. In this paper, we propose a AP2I system which is an adaptive PII scanning and discovery tool for helping the organization to automatically discover and manage PII for enhancing their privacy policy and satisfying PDPA compliance. Our AP2I consists of four key modules including (1) Converter converting text to csv format, (2) PII Scanner scanning the PII based on the extended function of Presidio, (3) Consent checker checking the analyzer and recognizer function of Presidio for the scanning process, and (4) PII inventory storing well-organized PII identifying PII records, data subject, and their source. We also conducted the experiment to demonstrate the efficiency of our proposed system.