Reviewerly/exHarmony huggingface dataset:
Bounded Functional Encryption for Turing Machines: Adaptive Security from General Assumptions

The recent work of Agrawal et al. [Crypto '21] and Goyal et al. [Eurocrypt '22] concurrently introduced the notion of dynamic bounded collusion security for functional encryption (FE) and showed a construction satisfying the notion from identity based encryption (IBE). Agrawal et al. [Crypto '21] further extended it to FE for Turing machines in non-adaptive simulation setting from the sub-exponential learining with errors assumption ( $$\textsf{LWE}$$ ). Concurrently, the work of Goyal et al. [Asiacrypt '21] constructed attribute based encryption (ABE) for Turing machines achieving adaptive indistinguishability based security against bounded (static) collusions from IBE, in the random oracle model. In this work, we significantly improve the state of art for dynamic bounded collusion FE and ABE for Turing machines by achieving adaptive simulation style security from a broad class of assumptions, in the standard model. In more detail, we obtain the following results:
A Study of a Metaverse Interdisciplinary Learning Community

In the current situation, our world has changed dramatically, mainly due to the emergence of coronavirus. This means that education has primarily been carried out online. This has involved creating a virtual learning environment through the metaverse for improving the quality of education and developing educational management systems. It is necessary to drive through mission-related activities, including teaching and learning. Student activities, professors, participation in local development, student leaders and student organizations are all an important part of driving development. This study aims to study and synthesize the interdisciplinary learning community framework. This start by studying the synthesis of elements of metaverse. There are 3 such elements: 1) software, 2) hardware, 3) content. In addition, there are five elements of the virtual learning community and the integration of interdisciplinary teaching classes.
CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs

Recent advances show that Generative Adversarial Networks (GANs) can synthesize images with smooth variations along semantically meaningful latent directions, such as pose, expression, layout, etc. While this indicates that GANs implicitly learn pixel-level correspondences across images, few studies explored how to extract them explicitly. In this work, we introduce Coordinate GAN (CoordGAN), a structure-texture disentangled GAN that learns a dense correspondence map for each generated image. We represent the correspondence maps of different images as warped coordinate frames transformed from a canonical coordinate frame, i.e., the correspondence map, which describes the structure (e.g., the shape of a face), is controlled via a transformation. Hence, finding correspondences boils down to locating the same coordinate in different correspondence maps. In CoordGAN, we sample a transformation to represent the structure of a synthesized instance, while an independent texture branch is responsible for rendering appearance details orthogonal to the structure. Our approach can also extract dense correspondence maps for real images by adding an encoder on top of the generator. We quantitatively demonstrate the quality of the learned dense correspondences through segmentation mask transfer on multiple datasets. We also show that the proposed generator achieves better structure and texture disentanglement compared to existing approaches. Project page: https://jitengmu.github.io/CoordGAN/
Enhancing Transferability of Adversarial Examples with Spatial Momentum

Many adversarial attack methods achieve satisfactory attack success rates under the white-box setting, but they usually show poor transferability when attacking other DNN models. Momentum-based attack is one effective method to improve transferability. It integrates the momentum term into the iterative process, which can stabilize the update directions by adding the gradients’ temporal correlation for each pixel. We argue that only this temporal momentum is not enough, the gradients from the spatial domain within an image, i.e. gradients from the context pixels centered on the target pixel are also important to the stabilization. For that, we propose a novel method named Spatial Momentum Iterative FGSM attack (SMI-FGSM), which introduces the mechanism of momentum accumulation from temporal domain to spatial domain by considering the context information from different regions within the image. SMI-FGSM is then integrated with temporal momentum to simultaneously stabilize the gradients’ update direction from both the temporal and spatial domains. Extensive experiments show that our method indeed further enhances adversarial transferability. It achieves the best transferability success rate for multiple mainstream undefended and defended models, which outperforms the state-of-the-art attack methods by a large margin of 10% on average.
Blockchain technology and its common threats

The work discusses technology such as Blockchain, which has become one of the most talked-about technologies in recent years. Blockchain is one of the loudest words in modern technology and gaining popularity every day. The application of the technology can be found in various fields and therefore the promise of the technology for the future is vast. This work will start by a brief explanation of what blockchain technology is. In addition, the scope of application of this technology. The work also will look into the threats that are out there and are making the technology of Blockchain venerable to the environment of modern-day. As with any new technology, Blockchain has its issues, some of them have a mild effect, and some are critical. Hence, understanding the common issues related to the Blockchain can be crucial when implementing the technology. This work will look at the common threats that the technology is facing today and are yet to be scoped when using it. The common threats can be classified into three main categories such as social threats, technological threats, and organisational threats. These categories cover issues like privacy, double spending, scalability and others. Nonetheless, the technology is well integrated into various fields
Website Phishing Detection Using Machine Learning Classification Algorithms

The growth and development of internet technology have made life a lot better and easier. It has increased the convenience of performing essential transactions with the click of a button using smart devices. Convenience comes with a price, the menace of cybercrimes. In recent years, the incidence of phishing websites has been one of the serious cyber security threats as it leads to the leakage of sensitive personal information. Phishing attacks have become a matter of major concern, and diligent actions and measures must be taken to curtail them. Detection of malicious websites can prevent phishing attacks to a great extent. In this paper, a URL feature-based website phishing detection technique is proposed to predict whether the websites are phishing or not with high accuracy. For this, we explored eight existing machine learning classification algorithms like extreme gradient boosting (XGBoost), random forest (RF), adaboost, decision trees (DT), K-nearest neighbours (KNN), support vector machines (SVM), logistic regression and naïve bayes (NB) to detect malicious websites. From the results it is obvious that XGboost exhibited the highest accuracy of 96.71%. Therefore, based on the implementation outcomes on the phishing website dataset, XGBoost performs better than the other classification algorithms in classifying the websites as phishing or legitimate.
Partial Domain Adaptation for Scene Classification From Remote Sensing Imagery

Although domain adaptation approaches have been proposed to tackle cross-regional, multitemporal, and multisensor remote sensing applications since they do not require any human interpretation in the target domain, most current works assume identical label space across the source and the target domains. However, in real-world applications, we often transfer knowledge from a large-scale dataset with rich annotations to a small-scale target dataset with scarcity of labels. In most cases, the label space of the source domain is usually large enough to subsume that of the target domain, which is termed partial domain adaptation. In this article, we propose a new partial domain adaptation algorithm for remote sensing scene classification and our proposed method contains three major parts. First, we employ a progressive auxiliary domain module to alleviate the negative transfer effect caused by outlier classes. Second, we adopt an improved domain adversarial neural network (DANN) with multiweights to better encourage domain confusion. Last but not least, we design an attentive complement entropy regularization to improve the prediction confidence for samples and avoid untransferable samples (such as the samples belonging to outlier classes in the source domain) being mistakenly classified. We collect three common remote sensing datasets to evaluate our proposed method. Our method achieves an average accuracy of 79.36%, which considerably outperforms other state-of-the-art partial domain adaptation methods with an average accuracy improvement of 1.90%–12.45% and attaining a 13.67% gain compared to the straightforward deep learning model (ResNet-50). The experiment results indicate that our approach shows promising prospects for solving more general and practical domain adaptation problems where the label space of the source domain subsumes that of the target domain.
Disentangling visual and written concepts in CLIP

The CLIP network measures the similarity between natural text and images; in this work, we investigate the entanglement of the representation of word images and natural images in its image encoder. First, we find that the image encoder has an ability to match word images with natural images of scenes described by those words. This is consistent with previous research that suggests that the meaning and the spelling of a word might be entangled deep within the network. On the other hand, we also find that CLIP has a strong ability to match nonsense words, suggesting that processing of letters is separated from processing of their meaning. To explicitly determine whether the spelling capability of CLIP is separable, we devise a procedure for identifying representation subspaces that selectively isolate or eliminate spelling capabilities. We benchmark our methods against a range of retrieval tasks, and we also test them by measuring the appearance of text in CLIP-guided generated images. We find that our methods are able to cleanly separate spelling capabilities of CLIP from the visual processing of natural images.
The BiLSTM-based synthesized speech recognition

Homer Dudley's VODER is considered the first attempt to synthesize human speech electronically by breaking it down into acoustic components. Fifty years later, Terminator 2 featured an example of human speech synthesized with artificial intelligence that was used to deceive a human. Speech synthesis is the artificial simulation of human speech using a computer or other device. The counterpart of the voice recognition, speech synthesis is mainly used to convert textual information into audio information so that a person can naturally interact with digital devices. For example, it is used in assistive technology to help visually impaired people read textual content. A separate direction is the use of speech synthesis to create a clone of a person's voice. Deepfake voice technology, also called voice cloning, has advanced to the point where it can accurately reproduce the human voice by mimicking intonation and other features of the speaker. And it can be used to harm a person. Attackers can employ it to fool voice authentication systems or create fake audio recordings to defame public figures, or combine voice clone with social engineering techniques to bamboozle people. This article discusses the architecture of a voice recognition system that will significantly reduce the possibility of fraud using deepfake voice.
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin and Eosin-Stained Histological Images

Recently, pruning deep neural networks (DNNs) has received a lot of attention for improving accuracy and generalization power, reducing network size, and increasing inference speed on specialized hardwares. Although pruning was mainly tested on computer vision tasks, its application in the context of medical image analysis has hardly been explored. This work investigates the impact of well-known pruning techniques, namely layer-wise and network-wide magnitude pruning, on the nuclei instance segmentation performance in histological images. Our utilised instance segmentation model consists of two main branches: (1) a semantic segmentation branch, and (2) a deep regression branch. We investigate the impact of weight pruning on the performance of both branches separately, and on the final nuclei instance segmentation result. Evaluated on two publicly available datasets, our results show that layer-wise pruning delivers slightly better performance than network-wide pruning for small compression ratios (CRs) while for large CRs, network-wide pruning yields superior performance. For semantic segmentation, deep regression and final instance segmentation, 93.75%, 95%, and 80% of the model weights can be pruned by layer-wise pruning with less than 2% reduction in the performance of respective models.
Identification of Key Concerns and Sentiments Towards Data Quality and Data Strategy Challenges Using Sentiment Analysis and Topic Modeling

In the era of Fourth Industrial Revolution, data and information became a valuable resource. In this data-driven economy, it is extremely important to maintain high level of data quality. Poor data quality can be a significant business cost and therefore data quality and consistency is a primary challenge for contemporary enterprises. There exists the need for concrete understanding of data quality which is the key concern among the data users. Hence, the study was carried out with the objective to analyse Twitter data to extract sentiments and opinions in unstructured texts and the key topics that are under consideration of Twitter users. Further, Text classification and topic modelling techniques have been performed to identify positive and negative sentiments and the key themes represented in polarized texts referring to data quality. In this study, two-step processes were followed to achieve the objective. In the first step, positive and negative sentiments were identified from Twitter feeds. In the second step, the Latent Dirichlet Allocation method was performed that allows to discover the keywords in the text corpuses that capture the recurring themes and is widely used to analyse large sets of polarized texts to identify the most common topics quickly and efficiently. The study contributes to text mining literature by providing a framework for analysing public sentiments. This can help to understand the key themes in negative sentiments related to data quality among the machine learning practitioners. Also, key concerns of public/data users could be highlighted and shared with larger community.
RSI-Grad-CAM: Visual Explanations from Deep Networks via Riemann-Stieltjes Integrated Gradient-Based Localization

Neural networks are becoming increasingly better at tasks that involve classifying and recognizing images. At the same time techniques intended to explain the network output have been proposed. Here we examine three such techniques: Gradient-based Class Activation Mapping (Grad-CAM), Integrated Gradients (IG), and Integrated Grad-CAM, and introduce a new technique, that we call Riemann-Stieltjes Integrated Grad-CAM (RSI-Grad-CAM) that overcomes some of the shortcomings of those and similar techniques. Like Grad-CAM, our method can be applied to any layer of the network, and like Integrated Gradients it is not affected by the problem of vanishing gradients. For efficiency, gradient integration is performed numerically at the layer level using a Riemann-Stieltjes sum approximation. Compared to Grad-CAM, heatmaps produced by our algorithm are better focused in the areas of interest, and their numerical computation is more stable.
Method cards for prescriptive machine-learning transparency

Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. Techniques such as Datasheets, AI FactSheets, and Model Cards have taken a mainly descriptive approach, providing various details about the system components. While the above information is essential for product developers and external experts to assess whether the ML system meets their requirements, other stakeholders might find it less actionable. In particular, ML engineers need guidance on how to mitigate potential shortcomings in order to fix bugs or improve the system's performance. We propose a documentation artifact that aims to provide such guidance in a prescriptive way. Our proposal, called Method Cards, aims to increase the transparency and reproducibility of ML systems by allowing stakeholders to reproduce the models, understand the rationale behind their designs, and introduce adaptations in an informed way. We showcase our proposal with an example in small object detection, and demonstrate how Method Cards can communicate key considerations that help increase the transparency and reproducibility of the detection model. We further highlight avenues for improving the user experience of ML engineers based on Method Cards.
One step ahead: mapping the Italian and German cybersecurity laws against the proposal for a NIS2 directive

With the COVID-19 pandemic accelerating digital transformation of the Single Market, the European Commission also speeded up the review of the first piece of European Union (EU)-wide cybersecurity legislation, the NIS Directive. Originally foreseen for May 2021, the Commission presented the review as early as December 2020 together with a Proposal for a NIS2 Directive. Almost in parallel, some Member States strengthened (or adopted) national laws beyond the scope of the NIS Directive to respond adequately to the fast-paced digital threat landscape. Against this backdrop, the article investigates the national interventions in the field of cybersecurity recently adopted by Italy and Germany. In order to identify similarities and divergences of the Italian and German national frameworks with the European Commission's Proposal for a NIS2 Directive, the analysis will focus on selected aspects extrapolated from the Commission Proposal, namely: i) the enlarged scope; ii) detailed cybersecurity risk-management measures; iii) more stringent supervisory measures; and, iv) stricter enforcement requirements, including harmonised sanctions across the EU. The article concludes that the national cybersecurity legal frameworks under scrutiny already match the core of the proposed changes envisaged by the NIS2 Proposal.
Video Anomaly Detection Based on Optical Flow Feature Enhanced Spatio–Temporal Feature Network FusionNet-LSTM-G

Video anomaly detection refers to the rapid identification and localization of anomalous events during video surveillance.It has become a challenging task due to the dependence of anomalous event definitions on specific scenes and the scarcity, diversity and non-exhaustiveness of anomalous events.This manuscript proposes a robust frame prediction-based method for video anomaly detection, it can solve the problems of underutilized spatio-temporal sequence information and difficulty in capturing subtle motions.Firstly, a spatial-temporal feature fusion network is constructed using FusionNet and LSTM.Secondly, feature optimization is performed on this spatio-temporal feature fusion network using optical flow feature enhancement.To further improve the robustness of the model, patch GAN is added to FusionNet-LSTM for adversarial training, and the objective function in training is optimized with apparent constraints and image similarity constraints to approach the real frame.Finally, to reduce the sequence error, a PNSR-based mean normalized anomaly score calculation is used in the manuscript.Experiments show that the average accuracy of the proposed method for anomalous event detection on UCSD Ped2 and CUHK Avenue datasets is 96.9% and 86.1% respectively, which is 1.5% and 1.0% better than the benchmark model U-Net，and the model has low computational complexity where FLOP is 6.196G and the number of parameters is 6.829M.It can meet the application requirements of real-time video anomaly detection and has great value for security video surveillance.
Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments

This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments. Existing end-to-end learning-based VLN methods struggle at this task as they focus mostly on utilizing raw visual observations and lack the semantic spatio-temporal reasoning capabilities which is crucial in generalizing to new environments. In this regard, we present a hybrid transformer-recurrence model which focuses on combining classical semantic mapping techniques with a learning-based method. Our method creates a temporal semantic memory by building a top-down local ego-centric semantic map and performs cross-modal grounding to align map and language modalities to enable effective learning of VLN policy. Empirical results in a photo-realistic long-horizon simulation environment show that the proposed approach outperforms a variety of state-of-the-art methods and baselines with over 22% relative improvement in SPL in prior unseen environments.
Research on Key Technologies of Garbage Classification Virtual Simulation Game Development Based on unity3d Technology

In recent years, the problems caused by garbage have become more and more serious, and many countries have begun to advocate the concept of environmental protection. China has also begun to implement the policy of waste classification, but the effect is not ideal. This is because the publicity of waste classification knowledge has not reached good expectations, and the traditional propaganda means such as static graphics and text can not give people a deep impression. Therefore, in order to better achieve the knowledge publicity of garbage classification, under the conditions of the continuous maturity of Internet technology and 5g technology, relying on virtual reality technology, combined with novel game mode and immersive game experience, through games, players' interest in learning garbage classification knowledge is increased, so as to improve the knowledge publicity efficiency of garbage classification. This paper discusses and studies a series of key technologies used in virtual simulation games, which use unity 3D as the development editor of the game engine, 3D Max as the model creation tool, shadow creator SDK as the VR technical support, and action one headset to display the game content.