Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for COVID-19 Related Tweets Using Deep Bi-Directional Transformers

This study uses a pre-trained Bi-directional Encoder Representations from Transformers (BERT) with an AdamW optimizer for sentiment analysis of COVID-19 related tweets. This is performed on around 32,000 tweets from an annotated dataset of 190 million tweets. The sentiment of each tweet was predicted between three different classes, negative, neutral, and positive. Under sampling was performed to balance out the data and the model was fine-tuned over 4 epochs. The resulting model was best at predicting negative sentiment and worst at predicting neutral sentiment. The resulting accuracy was found to be 75.15%, however, increasing the amount of data used would likely improve this significantly.
A Detection Method for Copy-Move Forgery Attacks in Digital Images

Digital images are carrying important information in many real-world applications such as surveillance, courts of law as evidence for crimes, journalism, scientific publications, and medical imaging. Manipulating these images becomes easy due to the paramount image processing and editing tools. Thus, the authentication of digital images becomes one of the principal problems in digital image forensics and attracts the attention of the research community. In this paper, we focus on the detection of copy-move forgery attacks in digital images. It is one of the most actively used attacks. It copies some region of the image and pastes it into one or more parts of the same image to cover or emphasize important scenes in the image. We propose a method to detect such forgery regions based on Speeded Up Robust Features (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) descriptors. Both fused features are matched and performed clustering to reduce the false positives using Hierarchical Agglomerative Clustering (HAC). The proposed method is tested on real-world copy-move datasets. Experimental results show that our method is robust against various geometric transformations and able to precisely determine the forged regions.
A Novel Threat Intelligence Information Extraction System Combining Multiple Models

The increasing number of cyberattacks has made the cybersecurity situation more serious. Thus, it is urgent to use cyber threat intelligence to deal with the complex and changing cyber environment. However, cyber threat intelligence usually exists in an unstructured form, and a huge amount of data poses a great challenge to security analysts. To this end, this paper proposes a novel threat intelligence information extraction system combining multiple models, which contains four key steps: entity extraction, coreference resolution, relation extraction, and knowledge graph construction. In the entity extraction task, a multihead self-attention mechanism is adopted to extract the dependency relationships between words. In the coreference resolution task, contextual information and mention embedding are fused to improve the mention representation. Meanwhile, features of different dimensions are extracted using a convolutional neural network. In the relation extraction task, additional features such as part of speech, mention width, entity type, and distance of entity pairs are incorporated to improve the embedding representation. Finally, a knowledge graph is constructed to explicitly present entities and their relationships. Experimental results indicate that compared with the baseline model, the F1 score of our model is improved by at least 8.87, 9.82, and 10.56 on entity extraction, coreference resolution, and relation extraction, respectively. The knowledge graph in Neo4j demonstrates the effectiveness of our system.
CLS-DETR: A DETR-series object detection network using classification information to accelerate convergence

The DETR network eliminates the hand-designed components by using the excellent Transformer architecture. The ambiguous meaning of the object query in the Transformer makes the DETR slow to converge. Existing works on improvements to the DETR tend to strengthen the relationship between the object query and position information in the feature map to reduce the ambiguity. However, both classification information and position information are essential components of the object query, so it is not comprehensive to reduce the ambiguity without considering the classification information. In our method, we novelly consider the influence of classification information in the object query on the DETR convergence rate. We propose CLS-DETR, which is the first to use the classification information of object query to optimize the generation of the object query iteratively. This proposed network strengthens the relationship between the object query and classification information and reduces the ambiguity. CLS-DETR accomplishes faster convergence compared with other DETR-series detection models on the challenging COCO dataset and achieved 45.3 AP in the standard 3× training schedule using the ResNet-50 FPN model, demonstrating the ability of clear classification information to accelerate the DETR convergence.
Sticks and STONES may build my bones: Deep learning reconstruction of limb rotations in stick figures

Monitoring and analyzing physical activity is becoming an important task in both clinical and non-clinical settings. To accomplish this desideratum, stick figures are often used as abstractions of human poses and movements by representing body segments as straight lines (sticks). Despite their straightforwardness, this minimalist representation is incomplete as it lacks the segments’ longitudinal rotations, and therefore, is insufficient for applications requiring full 3D kinematic data. We introduce STONES, an advanced machine learning approach for estimating longitudinal body segment rotations of based on stick figures defined from a minimal set of body points. Our approach relies on a recurrent deep neural network, which takes 3D joint positions from a minimalist stick figure representation, such as those acquired by conventional depth camera sensors, and completes it with accurate longitudinal segment rotations. We validated our approach via a test scenario based on exergaming activities (e.g., lunges, squats, and kicks), which are becoming an emerging trend in several healthcare sectors, and our estimations show a fit above 98% and mean errors of approximately 1 ∘. Our deep learning approach effectively surpasses other machine learning-based strategies and closely matches the accuracy of state-of-the-art motion capture systems while running at real-time speeds.
Small Sample Signal Modulation Recognition based on Higher-order Cumulants and CatBoost

It is difficult and costly to obtain sufficient label samples in an open environment, and the study of small sample problem has become an important direction in the field of signal modulation recognition. For the first time, we innovatively propose to use CatBoost to solve this. First, we extract high-order cumulants features from the I/Q signal, and then add slicing operations to make this feature suitable for algorithm training, and finally take advantage of CatBoost's high classification accuracy under small sample condition, to achieve effective recognition of small sample signals. The experiment obtained the results of comprehensive recognition accuracy of 93.3% and 95.1% when there are 20 and 200 samples in each type of 9 types of signals from 0 to 8dB, respectively. Compared with other traditional machine learning algorithms and deep learning algorithms, our method is more efficient.
Sketch recognition based on attention mechanism and improved residual network

Sketches are usually composed of simple strokes. Compared with the natural image, they lack the information of texture and color. Most of the existing works do not reduce the impact of blank region in sketches very well. This paper proposes a new deep convolutional neural network named Sketch Fusion Net (SFN) model to minimize the effect of the blank region and focus on information region. This model is mainly composed of Sketch Block Convolutional (SBConv) modules, the SBCnov module integrates an attention mechanism and an improved residual network. The first one can effectively extract the effective part of the sketch information. The other one uses multi-level feature combination strategy to extract richer semantic information, which can effectively relieve the problem of model degradation. Finally, two public datasets, namely the TU-Berlin and Sketchy, are used for sketch recognition experiments. The results demonstrate that this proposed method improves the recognition accuracy by 2.1% and 7.2% over several state-of-the-art methods and yields promising results.
A Robust Method for Electrical Equipment Infrared and Visible Image Registration

The integration of infrared and visible images can take advantage of temperature information from infrared modality and sharp appearance from visible modality, and therefore it is helpful to improve the accuracy of localization and fault diagnosis of electrical equipment. A key step towards integration analysis is to register the images in infrared and visible modalities. In this paper, we propose a new method for infrared and visible image registration. In order to deal with large difference between these two modalities, we first transform both infrared and visible images into radiation-invariant maps. Then, LoFTR, which is a self-attention based deep neural network, is adopted to extract and match features based on the radiation-invariant maps. Finally, we utilize a progressive sample consensus (PROSAC) algorithm to estimate the transformation parameters, based on which the infrared image can be transformed into the corresponding visible image coordinates. Experiments on an electrical equipment dataset show that our proposed method is robust to both radiation and geometric variations.
Using Big Data Analytics for Sentiment Analysis to Explore Team Communication Dynamics in Human Machine Interactions for Team Situational Awareness

The team communication dynamics is one of the crucial factors that can provide a plethora of information regarding the evolution of situational awareness and in turn highlight the pivotal moments for mission success in any combat training simulation scenario. Specifically, in this work, we study the team communication dynamics data collected from a firefighting simulation. The large volume of communication between individual team members recorded throughout the simulation training provides an ideal basis for the use of Big Data analytics and sentiment analysis to derive deeper insights on various levels of situational awareness observed throughout the simulations. We are interested in understanding the relationship between specific cluster of words or sentiments that are associated with various levels of situational awareness. In particular, we explore the relationship between team communication frequency dynamics, and its relationship to team situational awareness and team performance in a fire-fighting mission success scenario. The outcomes of this research is revolutionary in terms of early identification of team success factors, providing support strategies for improvement of team performance and corresponding team situational awareness in tasks. We experimentally demonstrate that the introduction of Big Data analytics and sentiment analysis in human factor research can invariably boost the situational awareness and team communication dynamics nuances that might otherwise be overlooked. Thus, this research highlights a new avenue for leveraging the team communication dynamics for better situational awareness and mission success objectives.
Maze Routing: An Information Privacy-aware Secure Routing in Internet of Things for Smart Grid

To ensure the security and reliable transmission of private information in Internet of Things for Smart Grid (for short SG-IoT), we proposed a privacy-aware secure routing method (i.e., Maze Routing) based on a novel directional random routing paradigm, which consists of a direction-determined virtual global routing mode and a physical local routing mode. Firstly, for an in-transit packet at the current node, all k-hop nodes are defined as its k-hop wavefronts, and many x-hop wavefronts within k hops are further gathered as a hyper next-hop set. Then, under the direction-determined virtual global routing mode, the packet randomly selects one of elements from the next-hop set by a priority probability as its global next-hop node according to its privacy awareness and End-to-End Quality of Service (E2E QoS) requirement, so as to build a virtual global routing path with a definite direction determined by the destination of this packet. Moreover, under the physical local routing mode, a local pathlet for any two consecutive global next-hop nodes can be built in an ordinary shortest-first routing scheme, then such many pathlets are wired in sequence to constitute a path in whole from source node to destination. Under several loT application scenarios within smart grid, the simulations results show that the proposed maze routing outperforms the existing shortest-first routing method in terms of privacy protection, E2E QoS and communication overhead.
Comparative Simulator Experiment on Preventing Motion Sickness by Aromatherapy Inhalation Based on MISC and EEG

To determine the preventive effects of four fragrance factors against motion sickness, an experiment was conducted inside a driving simulator with 20 subjects. The subjects sat in the driver’s seat in the simulator that drove fully autonomously. While driving, electroencephalogram (EEG) and MIsery SCale (MISC) data were acquired. The results of the experiment showed that the fragrances of peppermint, ginger, and ginger + lavender were effective in preventing motion sickness, and peppermint + bergamot and ginger + lavender were effective for relaxation.
The visual art education mediated by technologies: situations and teaching strategies in the midst of the COVID-19 pandemic

The teaching of visual arts during the COVID-19 pandemic is the subject of this writing, which seeks to point out situations and strategies used by teachers who worked in this area of knowledge in the school context, during the years 2020 and 2021. Interviews were held with four professors who worked in different institutions and teaching networks, so that it was possible to understand similarities and differences between them. The aforementioned period was marked in school education by the use of TDIC and remote classes. Several and distinct experiences were recorded by the professors, who, in general, had to dedicate themselves to so many different demands that the specific practices of the visual Arts ended up becoming less evident.
Using Hopfield Networks to Correct Instruction Faults

Fault injection attacks pose an important threat to security-sensitive applications, such as secure communication and storage. By injecting faults into instructions, an attacker can cause information leakage or denial-of-service. Hence, it is important to secure the sensitive parts not only by detecting faults in the executed instructions but also by correcting them. In this work, we propose a hardware detection and correction module based on Hopfield networks. Our module is connected to the instruction buffer and validates all fetched instructions. In case faults are detected, faulty instructions are replaced by corrected ones. Experimental results on a small RISC-V processor and two RSA implementations show that we achieve near perfect detection and around 70% accurate correction with 9% area overhead. This correction rate is enough to secure some implementations for all considered attacks.
Image Similarity Measurement for The Quality Control of Electricity Substation Inspection

The routine inspection of an electricity substation helps to detect faults and repair equipment in time, ensuring the substation to work safely. However, due to irregular operations, some inspectors may miss to capture images at certain spots while take multiple similar images at the same spots. In order to make control of the inspection quality, we design an algorithm to find such situation automatically. Specifically, given two images, we design a registration-based method to evaluate the affine transformation between two images, then we evaluate the averaged corner error by comparing the image transformed with respect to the estimated affine transformation to an identical transformation. Finally, we screen out the similar images that are small in the averaged corner error. These images are very likely to be taken at the same inspection spot. We conduct experiments on a dataset collected during one routine inspection of a whole substation. Experimental results show that our method is effective to screen out similar images, helping to build an automatic quality control process of the routine inspection.
A Flexible Instruction-based Post-quantum Cryptographic Processor with Modulus Reconfigurable Arithmetic Unit for Module LWR&amp;E

Post-quantum cryptography (PQC) uses novel difficult mathematical principles to defend the cracking of quantum computers which threaten the traditional crypto system such as Rivest–Shamir– Adleman (RSA) and elliptic curves cryptography (ECC). The lattice-based PQC schemes are currently the most potential candidates. The software methods of these schemes are usually low speed, and optimized dedicated hardware design can accelerate the algorithms. Saber and Kyber are two PQC algorithms which are based on difficult lattice problems of “Module Learning with Rounding” (M-LWR) and “Module Learning with Errors” (M-LWE). They have different modulus computing domains, which are $2 ^{13}$ and prime 3329 respectively. These characteristics lead to complicated solution and slow operation of conventional implementation, which is not conducive to resource efficiency and flexibility. In this work, we proposed a reconfigurable arithmetic unit with variable modulus domains, and combined with custom instruction-set architecture to design a flexible crypto processor for M-LWR and M-LWE. The work achieved the flexible implementation of variable parameters and instruction programming under the strategy of resource efficiency and performance trade-off, and verified on the FPGA platform.
Technical Aspects of Metaverse Development for Batik SMEs Exhibitions

Lasem Batik, which is famous for its uniqueness in the 3 ethnic elements, is currently experiencing considerable challenges due to changes in traveler routes between provincial capitals. Batik craftsmen need events and innovations to showcase their products as before the Covid-19 pandemic, to attract the attention of buyers at the local and global levels. One of the innovations that make it possible to showcase their products and meet with various parties at the local and global level is Metaverse. Metaverse is seen as a solution to display Batik products in their entirety without having to be disturbed by road access barriers to their village and the increasing prices of airline tickets between countries to their countries. The metaverse that was developed to organize the Batik exhibition requires technical details that need to be prepared. This paper explores the technical aspects of developing Metaverse for Batik exhibitions that can result in business transactions.
A Case Study of Human-AI Interactions Using Transparent AI-Driven Autonomous Systems for Improved Human-AI Trust Factors

Human interactions with Artificial Intelligence (AI) have become a ubiquitous phenomenon in our society. It plays a major role in defense and espionage in the context of AI-based autonomous systems such as drones and ground robots that are sent to the front lines to navigate terrains, map the surroundings, and identify adversaries in the environment. In these types of scenarios, hostages may be spread out over a larger geographical area. Reliance on ground vision alone may not only be obscured and insufficient but also can expose soldiers or human rescuers to danger. Since time is of great essence, such an intense situation calls for decisions to be made swiftly and efficiently as the lives of hostages are at stake. This makes such a scenario ideal to evaluate the effectiveness of an AI-driven drone autonomous system for combat search and rescue (CSAR) missions. Here the focus is to gather information about the operational environment (detect hostages and other targets-of-interest) in a short amount of time that spans a varying geographic terrain. This allows for targeted rescue operations with AI-informed decision-making outcomes to ensure that the rescuers at ground level can be directed along the most optimal path, and casualties will be either minimized or entirely averted. In this work, we study the feasibility of our drone-based AI target detection autonomous system to evaluate the classification accuracy of multi-view targets-of-interest in the CSAR environment. The overarching goal of this study is to understand the role of AI-driven autonomous systems as an assistive support tool for efficient, swift and accurate decision making, navigating a complex and ever-changing environment, and identification of targets.
Energy Efficient BNN Accelerator using CiM and a Time-Interleaved Hadamard Digital GRNG in 22nm CMOS

In recent years, Neural Networks (NNs) have achieved tremendous success in a variety of fields, such as computer vision, natural language processing, speech recognition, autonomous driving, and healthcare [1] –[4]. However, conventional NNs rely heavily on large, labeled training datasets, which can lead to overfitting and overconfident decision making (especially when faced with unfamiliar, out-of-distribution inputs) [1] –[4]. Unlike conventional NNs, the weights of Bayesian Neural Network (BNNs) are represented by probability distributions, providing a mathematical framework to quantify the uncertainties in a model’s final prediction [3]. These uncertainty estimates allow BNNs to mitigate overfitting issues, enable training with smaller datasets, and help increase overall model accuracy through the implicit use of stochastic rounding [5]. Figure 1 shows an example with a BNN version of LeNet-5 [6], where weights of the network are represented by Gaussian distributions. The ambiguous digit is classified as a ‘5’ and ‘3’ with probabilities of 80% and 20%, respectively. However, these uncertainty estimates come at great computational cost, as multiple forward inference passes are required to generate the necessary posterior distributions. As such, BNNs require not only efficient, high-performance multiply-accumulation (MAC), but an efficient Gaussian Random Number Generator (GRNG) with high-quality statistics as well.
Digital creativity in STEM education: the impact of digital tools and pedagogical learning models on the students’ creative thinking skills development

This paper investigates the impact of digital creativity educational practices on the improvement of academic performance and creative thinking development of Chinese high school students. The study defined the importance of the concept of STEM learning in the development of modern skills needed to live and work in a digital socio-economic change caused by the development of Industry 4.0. An educational experiment involving 60 9th grade Chinese students evaluated the impact of digital creativity learning practices on the development of students’ creative thinking on such indicators as fluency, flexibility, originality, detailing, and metaphoricity. The impact of digital creative learning practices on the performance of students in the control group and the experimental group was assessed. The analysis of the results of practical research on the impact of digital tools and pedagogical models of learning on the acquisition of students’ creative thinking skills was conducted, which proved that the digital environment is the optimal place for students’ creative manifestations and provides opportunities to improve creative thinking skills by implementing learning practical activities using innovative tools for the manifestation of digital creative skills.