Reviewerly/exHarmony huggingface dataset:
Efficient Language-Guided Reinforcement Learning for Resource-Constrained Autonomous Systems

In this paper, we propose an energy-efficient architecture which is designed to receive both images and text inputs as a step towards designing reinforcement learning agents that can understand human language and act in real-world environments.We evaluate our proposed method on three different software environments and a low power drone named Crazyflie to navigate towards specified goals and avoid obstacles successfully.To find the most efficient language-guided RL model, we implemented the model with various configurations of image input sizes and text instruction sizes on the Crazyflie drone GAP8 which consists of 8 RISC-V cores.The task completion success rate and onboard power consumption, latency, and memory usage of GAP8 are measured and compared with Jetson TX2 ARM CPU and Raspberry Pi 4. The results show that by decreasing 20% of input image size we achieve up to 78% energy improvement while achieving an 82% task completion success rate.
Detecting Cross-language Memory Management Issues in Rust

Rust is a promising system-level programming language that can prevent memory corruption bugs using its strong type system and ownership-based memory management scheme. In practice, programmers usually write Rust code in conjunction with other languages such as C/C++ through Foreign Function Interface (FFI). For example, many notable projects are developed using Rust and other programming languages, such as Firefox, Google Fuchsia OS, and the Linux kernel. Although it is widely believed that gradually re-implementing security-critical components in Rust is a way of enhancing software security, however, using FFI is inherently unsafe. In this paper, we show that memory management across the FFI boundaries is error-prone. Any incorrect use of FFI may corrupt Rust’s ownership system, leading to memory safety issues. To tackle this problem, we design and build FFIChecker, an automated static analysis and bug detection tool dedicated to memory management issues across the Rust/C FFI. We evaluate our tool by checking 987 Rust packages crawled from the official package registry and reveal 34 bugs in 12 packages. Our experiments show that FFIChecker is a useful tool to detect real-world cross-language memory management issues with a reasonable amount of computational resources.
The Devil Is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models

Deep Generative Models (DGMs) are a popular class of models which find widespread use because of their ability to synthesise data from complex, high-dimensional manifolds. However, even with their increasing industrial adoption, they have not been subject to rigorous security analysis. In this work we examine backdoor attacks on DGMs which can significantly limit their applicability within a model supply chain and cause massive reputation damage for companies outsourcing DGMs form third parties. DGMs are vastly different from their discriminative counterparts and manifestation of attacks in DGMs is largely understudied. To this end we propose three novel training-time backdoor attacks which require modest computation effort but are highly effective. Furthermore, we demonstrate their effectiveness on large-scale industry-grade models across two different domains - images (StyleGAN) and audio (WaveGAN). Finally, we present an insightful discussion and prescribe a practical and comprehensive defense strategy for safe usage of DGMs.
Large Scale Analysis of DoH Deployment on the Internet

DNS over HTTPS (DoH) is one of the standards to protect the security and privacy of users. The choice of DoH provider has controversial consequences, from monopolisation of surveillance to lost visibility by network administrators and security providers. More importantly, it is a novel security business. Software products and organisations depend on users choosing well-known and trusted DoH resolvers. However, there is no comprehensive study on the number of DoH resolvers on the Internet, its growth, and the trustworthiness of the organisations behind them. This paper studies the deployment of DoH resolvers by (i) scanning the whole Internet for DoH resolvers in 2021 and 2022; (ii) creating lists of well-known DoH resolvers by the community; (iii) characterising what those resolvers are, (iv) comparing the growth and differences. Results show that (i) the number of DoH resolvers increased 4.8 times in the period 2021–2022, (ii) the number of organisations providing DoH services has doubled, and (iii) the number of DoH resolvers in 2022 is 28 times larger than the number of well-known DoH resolvers by the community. Moreover, 94% of the public DoH resolvers on the Internet are unknown to the community, 77% use certificates from free services, and 57% belong to unknown organisations or personal servers. We conclude that the number of DoH resolvers is growing at a fast rate; also that at least 30% of them are not completely trustworthy and users should be very careful when choosing a DoH resolver.
Traditional Machine Learning and Deep Learning-based Text Classification for Turkish Law Documents using Transformers and Domain Adaptation

Natural Language Processing (NLP) is an interdisciplinary field between linguistics and computer science. Its main aim is to process natural (human) language using computer programs. Text classification is one of the main tasks of this field, and they are widely used in many different applications such as spam filtering, sentiment analysis, and document categorization. Nonetheless, there is only very little text classification work in the law domain and even less for the Turkish language. This may be attributed to the complexity within the domain. The length, complexity of documents, and use of extensive technical jargon are some of the reasons that distinguish this domain from others. Similar to the medical domain, understanding these documents requires extensive specialization. Another reason can be the scarcity of publicly available datasets. In this study, we compile sizeable unsupervised and supervised datasets from publicly available sources and experiment with several classification algorithms ranging from traditional classifiers to much more complicated deep learning and transformer-based models along with different text representations. We focus on classifying Court of Cassation decisions for their crime labels. Interestingly, the majority of the models we experiment with could be able to obtain good results. This suggests that although understanding the documents in the legal domain is complicated and requires expertise from humans, it may be relatively easier for machine learning models despite the extensive presence of the technical terms. This seems to be especially the case for transformer-based pre-trained neural language models which can be adapted to the law domain, showing high potential for future real-world applications.
SQUIRREL: A framework for sequential group recommendations through reinforcement learning

Nowadays, sequential recommendations are becoming more prevalent. A user expects the system to remember past interactions and not conduct each recommendation round as a stand-alone process. Additionally, group recommendation systems are more prominent since more and more people are able to form groups for activities. Subsequently, the data that a group recommendation system needs to consider becomes more complicated — historical data and feedback for each user, the items recommended and ultimately selected to and by the group, etc. This makes the selection of a group recommendation algorithm to be even more complex. In this work, we propose the SQUIRREL framework — SeQUentIal Recommendations with ReinforcEment Learning, a model that relies on reinforcement learning techniques to select the most appropriate group recommendation algorithm based on the current state of the group. At each round of recommendations, we calculate the satisfaction of each group member, how relevant each item in the group recommendation list is for each user, and based on this the model selects an action, that is, a recommendation algorithm out of a predefined set that will produce the maximum reward. We present a sample of methods that can be used; however, the model is able to be further configured with additional actions, different definitions of rewards or states. We perform experiments on three real world datasets, 20M MovieLens, GoodReads and Amazon, and show that SQUIRREL is able to outperform all the individual recommendation methods used in the action set, by correctly identifying the recommendation algorithm that maximizes the reward function utilized.
Model transparency and interpretability: survey and application to the insurance industry

The use of models, even if efficient, must be accompanied by an understanding at all levels of the process that transforms data (upstream and downstream). Thus, needs increase to define the relationships between individual data and the choice that an algorithm could make based on its analysis (e.g. the recommendation of one product or one promotional offer, or an insurance rate representative of the risk). Model users must ensure that models do not discriminate and that it is also possible to explain their results. This paper introduces the importance of model interpretation and tackles the notion of model transparency. Within an insurance context, it specifically illustrates how some tools can be used to enforce the control of actuarial models that can nowadays leverage on machine learning. On a simple example of loss frequency estimation in car insurance, we show the interest of some interpretability methods to adapt explanation to the target audience.
InvGAN: Invertible GANs

Generation of photo-realistic images, semantic editing and representation learning are only a few of many applications of high-resolution generative models. Recent progress in GANs have established them as an excellent choice for such tasks. However, since they do not provide an inference model, downstream tasks such as classification cannot be easily applied on real images using the GAN latent space. Despite numerous efforts to train an inference model or design an iterative method to invert a pre-trained generator, previous methods are dataset (e.g. human face images) and architecture (e.g. StyleGAN) specific. These methods are nontrivial to extend to novel datasets or architectures. We propose a general framework that is agnostic to architecture and datasets. Our key insight is that, by training the inference and the generative model together, we allow them to adapt to each other and to converge to a better quality model. Our InvGAN, short for Invertible GAN, successfully embeds real images in the latent space of a high quality generative model. This allows us to perform image inpainting, merging, interpolation and online data augmentation. We demonstrate this with extensive qualitative and quantitative experiments.
Will Bitcoin ever become less volatile?

We examine the drivers of Bitcoin volatility and discuss possible future developments, specifically what conditions need to be met for the volatility to decrease. Our instrumental variables analysis implies there needs to be a considerable inflow of small users into the system who are ideally not exchange traders and they do perform small transfers. Increasing exchange volume, on-chain transfers value, and Bitcoin price by themselves increase volatility of the cryptoasset.
Integrating the Pre-trained Item Representations with Reformed Self-attention Network for Sequential Recommendation

The conventional sequential recommendation tends to recommend an item to a sequence due to the concurrence of this item in other similar sequences. It is a typical collaborative filtering recommendation strategy that suffers from the data sparsity problem. Integrating the item-side information into the recommendation model can alleviate the data sparsity problem and avoid the privacy issue. However, roughly fusing item-side information in item embeddings to optimize for final recommendation accuracy might lose the objective associations among items. Thus, this paper proposes a self-supervised learning task on item-side information to generate the item representation space in advance of the recommendation task. The space can gather similar items and separate the different items. Then we reform a recently successful sequential recommendation method, self-attention network, by integrating an untrainable mechanism to fully take advantage of the pre-trained item representation space. The two procedures comprise a hybrid recommendation framework to achieve collaborative filtering and content-based recommendation strategies. Extensive experiments conducted on two benchmark datasets demonstrate the superiority of our proposed methods over other baseline methods.
Spatio-Temporal Mogrifier LSTM and Attention Network for Next POI Recommendation

The next point-of-interest (POI) recommendation is indispensable in enhancing the richness of users’ lives and helping service providers achieve more economic earnings. Recurrent Neural Network (RNN) based methods are remarkable in learning users’ long-term or short-term behavioral dependencies. However, existing RNN-based methods lack sufficient interaction with their contexts, and at the same time, ignore the importance of non-consecutive POIs with different degrees for understanding users’ behaviors. In order to solve these problems, we propose a novel Spatio-Temporal model based on mogrifier LSTM and attention network (named STMLA) for next POI recommendation. The STMLA model builds a parallel structure to process the users’ check-in sequences through the mogrifier LSTM and the multi-head attention network, which can achieve better contextual interaction while selectively considering nonconsecutive factors with different degrees of significance. Our STMLA algorithm explicitly integrates temporal and spatial information to capture users’ long-term and short-term preferences, incorporating spatial information to build the Location-Saltant algorithm. Through extensive experiments on several real-world datasets, we demonstrate that our model outperforms the existing state-of-the-art methods in the next POI recommendation task.
Quality Assessment of UGC Videos Based on Decomposition and Recomposition

The prevalence of short-video applications imposes more requirements for video quality assessment (VQA). User-generated content (UGC) videos are captured under an unprofessional environment, thus suffering from various dynamic degradations, such as camera shaking. To cover the dynamic degradations, existing recurrent neural network-based UGC-VQA methods can only provide implicit modeling, which is unclear and difficult to analyze. In this work, we consider explicit motion representation for dynamic degradations, and propose a motion-enhanced UGC-VQA method based on decomposition and recomposition. In the decomposition stage, a dual-stream decomposition module is built, and VQA task is decomposed into single frame-based quality assessment problem and cross frames-based motion understanding. The dual streams are well grounded on the two-pathway visual system during perception, and require no extra UGC data due to knowledge transfer. Hierarchical features from shallow to deep layers are gathered to narrow the gaps from tasks and domains. In the recomposition stage, a progressively residual aggregation module is built to recompose features from the dual streams. Representations with different layers and pathways are interacted and aggregated in a progressive and residual manner, which keeps a good trade-off between representation deficiency and redundancy. Extensive experiments on UGC-VQA databases verify that our method achieves the state-of-the-art performance and keeps a good capability of generalization. The source code will be available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sissuire/DSD-PRO</uri> .
Context-aware IoT Service Recommendation: A Deep Collaborative Filtering-based Approach

The advantages of Service-Oriented Architecture (SOA) combined with the emerging and diffusion of the Internet of Things (IoT) instances have given birth to a new paradigm for IoT components integration, i.e., Service-Oriented IoT. Microservices especially have been widely used to deliver IoT services due to their lightweight implementation and distributed nature. With the continuous increase in IoT services available on the Internet, the selection of services becomes difficult. Furthermore, IoT services are often featured with rich contexts and OpenAPI descriptions, which impede service recommendation approaches that are designed for WSDL-based Web services or mashup services. To address this challenging issue, we propose a context-aware IoT service recommendation approach called DFORM for proactive service provision. DFORM considers both functional features of OpenAPI descriptions and contextual features of IoT environments, and leverages a deep collaborative filtering-based recommendation model to learn the feature representations and capture the interactions between users and services. We conduct a series of experiments to evaluate the recommendation performance of DFORM and the experimental results show that DFORM is effective in IoT service recommendation and outperforms state-of-the-art techniques.
AppClassNet

The recent success of Artificial Intelligence (AI) is rooted into several concomitant factors, namely theoretical progress coupled with abundance of data and computing power. Large companies can take advantage of a deluge of data, typically withhold from the research community due to privacy or business sensitivity concerns, and this is particularly true for networking data. Therefore, the lack of high quality data is often recognized as one of the main factors currently limiting networking research from fully leveraging AI methodologies potential. Following numerous requests we received from the scientific community, we release AppClassNet, a commercial-grade dataset for benchmarking traffic classification and management methodologies. AppClassNet is significantly larger than the datasets generally available to the academic community in terms of both the number of samples and classes, and reaches scales similar to the popular ImageNet dataset commonly used in computer vision literature. To avoid leaking user- and business-sensitive information, we opportunely anonymized the dataset, while empirically showing that it still represents a relevant benchmark for algorithmic research. In this paper, we describe the public dataset and our anonymization process. We hope that AppClassNet can be instrumental for other researchers to address more complex commercial-grade problems in the broad field of traffic classification and management.
Specific Emitter Identification Based on Self-Supervised Contrast Learning

The current deep learning (DL)-based Specific Emitter Identification (SEI) methods rely heavily on the training of massive labeled data during the training process. However, the lack of labeled data in a real application would lead to a decrease in the method’s identification performance. In this paper, we propose a self-supervised method via contrast learning (SSCL), which is used to extract fingerprint features from unlabeled data. The proposed method uses large amounts of unlabeled data to constitute positive and negative pairs by designing a composition of data augmentation operations for emitter signals. Then, the pairs would be input into the neural network (NN) for feature extraction, and a contrastive loss function is introduced to drive the network to measure the similarity among data. Finally, the identification model can be completed by fixing the parameters of the feature extraction network and fine-tuning with few labeled data. The simulation experiment result shows that, after being fine-tuned, the proposed method can effectively extract fingerprint features. When the SNR is 20 dB, the identification accuracy reaches 94.45%, which is better than the current mainstream DL approaches.
IPSFS: Intuitionistic, pythagorean, and spherical fuzzy similarity computation package in R

<h2>Abstract</h2> Finding similarities between objects is an important task in various fields of research. Advanced fuzzy logic-based similarity techniques are widely used in the literature for computing the similarity between objects. Despite the development of several similarity techniques in recent years, no fuzzy similarity measure package is available that can integrate all similarity techniques and assist a wide range of researchers, practitioners in performing their tasks efficiently. The package IPSFS is developed to compute the similarity among different objects. It includes several useful functions to compute the similarity between objects or items based on their intuitionistic, pythagorean, and spherical fuzzy relationships.
Efficient and scalable patients clustering based on medical big data in cloud platform

With the outbreak and popularity of COVID-19 pandemic worldwide, the volume of patients is increasing rapidly all over the world, which brings a big risk and challenge for the maintenance of public healthcare. In this situation, quick integration and analysis of the medical records of patients in a cloud platform are of positive and valuable significance for accurate recognition and scientific diagnosis of the healthy conditions of potential patients. However, due to the big volume of medical data of patients distributed in different platforms (e.g., multiple hospitals), how to integrate these data for patient clustering and analysis in a time-efficient and scalable manner in cloud platform is still a challenging task, while guaranteeing the capability of privacy-preservation. Motivated by this fact, a time-efficient, scalable and privacy-guaranteed patient clustering method in cloud platform is proposed in this work. At last, we demonstrate the competitive advantages of our method via a set of simulated experiments. Experiment results with competitive methods in current research literatures have proved the feasibility of our proposal.
A Data Connector Store for International Data Spaces

The International Data Spaces Association (IDSA) has promoted the idea of International Data Spaces as a place for companies to share data with trust and security enforced by software and organizational competence. There has been considerable progress in delivering corporate guidelines, technical specifications, and software components available for testing and deploying applications to support IDS-based ecosystems, such as the IDS data connectors classified by the Fraunhofer Institute. However, full implementation of IDS applications seems still complex and expensive for small and medium enterprises (SMEs). A possible strategy to deal with such an issue is to break the IDSA specification’s complexity into smaller pieces and build small IDS ecosystems formed by its core business roles (e.g., data owners, users, and broker service providers). In this context, this paper addresses the problem of designing an application to support the broker service provider’s role in operating in an IDS-based ecosystem. This research, therefore, follows a Design Science approach in a three-step process. First, it investigates problems of practical relevance elicited from the IDSA guidelines in combination with requirements provided by representatives of the Dutch Logistics sector. Second, it gives design to tackle the problem by combining Semantic Web, Linked Data, and Enterprise Architecture modeling artifacts. Last, it validates the architecture of the broker service provider’s application by demonstrating its technical feasibility, innovation, and software integration.