Reviewerly/exHarmony huggingface dataset:
Multi-layer Attention Social Recommendation System Based on Deep Reinforcement Learning

The recommendation system based on deep reinforcement learning recommends interesting content to users through the interaction of recommendation agents and usersThis is to inform you that corresponding author has been identified as per the information available in the Copyright form.. However, most recommender systems based on deep reinforcement learning often face two limitations: (1) sparse user feedback data that makes the recommendation agent unable to accurately capture the user’s dynamic preferences; and (2) users and items are isolated from each other due to the limitation of unstructured representation. To address this situation, this paper proposes a multi-layer attention social recommendation method based on deep reinforcement learning by fusing social network and user-item bipartite graph to form a heterogeneous information network. This method uses subgraphs in the heterogeneous information network to structurally represent users and items through a variant of the graph attention network. By doing so, users and items can perceive neighborhood information in the heterogeneous information network and enhance the correlation between nodes. At the same time, it can also avoid the repeated transmission of irrelevant nodes. Additionally, the attention mechanism is adopted in the graph attention network to reduce the influence of noisy nodes in the heterogeneous information network. Corresponding weights are given to the neighborhood information, and nodes with noise are given smaller weights. Then, the external attention mechanism is used to adjust the weight of historical items in the state information, which realizes the selective attention of different users to different items and generates the user’s preference representation. Finally, the deep reinforcement learning method is used to simulate the interaction between the recommendation system and the user. This method adapts to the dynamic changes of user preferences and considers the long-term rewards brought by the recommended items. Experimental results show that this method can alleviate the above two problems and provide users with more accurate recommended items.
The Influence of Social Media on Developing English Reading Skills of Saudi Universities Students

Social media has definitely become an integral part of our lives especially among university students. This technology has a wide impressive effect on how people learn and communicate with each other. However, the application of social media to learning especially for English department Saudi university students, is not fully explored. Therefore, the current study aims at mapping whether social media affects students’ reading competence positively or negatively. The study also seeks to find out if there are any significant differences in the students’ competence in reading as a result of using social media with NBU students due to gender, social media, reading skills and geographical distribution. The current study used quantitative methods using a survey instrument to gather descriptive data regarding the perceptions of (n=900) randomly chosen Saudi university students. Questionnaires with Likert scale questions were used in the study. The questionnaire was divided into two main parts. The first part began with two general questions about the number of years by which social media was used to perform general tasks and to perform specific English tasks. The second part was concerned with attitudes towards using social media, impact that social media has on reading competence. The data was analyzed using the Statistical Package for Social Sciences (SPSS) to obtain accurate results. Analysis of the collected data showed that the majority of the participants believed strongly in the pedagogical values and benefits of using social media as an ELT tool in their classrooms especially in improving reading skills. The findings also indicate that social networking websites and applications (e.g.Facebook, Youtube , Twitter, Skype) have a positive impact on learning English as a foreign language.
Reducing the Teacher-Student Gap via Elastic Student

The application of knowledge distillation (KD) has shown promise in transferring knowledge from a larger teacher model to a smaller student model. Nevertheless, a prevalent phenomenon in knowledge distillation is that student performance decreases when the teacher-student gap becomes large. Our contention is that the degradation from teacher to student is predominantly attributable to two gaps, namely the capacity gap and the knowledge gap. In this paper, we introduce Elastic Student Knowledge Distillation (ESKD), an innovative method that comprises Elastic Architecture and Elastic Learning to bridge the two gaps. The Elastic Architecture temporarily increases the number of student’s parameters during training and subsequently reverts to its original size while inference. It improves the learning ability of the model without increasing the cost at inference time. The Elastic Learning strategy introduces mask matrix and progressive learning strategies that facilitates the student in comprehending the intricate knowledge of the teacher and accomplishing the effect of regularization. We conducted extensive experiments on CIFAR-100 and ImageNet datasets, demonstrating that ESKD outperforms existing methods while preserving computational efficiency.
Unveiling Cybersecurity Threats from Online Chat Groups: A Triple Extraction Approach

In recent years, instant messaging software has become a popular platform for hackers to exchange knowledge and discuss cybersecurity issues. To trace the source of key hackers and identify potential cybersecurity threats, it is necessary to extract relational triples from hacker dialogues in chat logs. In this paper, we propose a feasible scheme for extracting cybersecurity knowledge triples from an extensive corpus of diverse chat data. We developed a heuristic algorithm based on the BERT next sentence prediction task to separate sequential and asynchronous chat logs into shorter dialogues and disentangle these threads within them, which can improve the accuracy of the subsequent relation extraction process. We also annotated a dialogue relation extraction dataset and developed a relation extraction model tailored for cybersecurity domain. Experimental results demonstrate that our average F1 scores on the thread disentanglement task and the dialogue relation extraction task are 74.9 and 88.4, respectively.
The Minimal Negated Model Semantics of Assumable Logic Programs

Assumable Logic Programming (ALP) extends the regular logic programs with an assumption operator $$\textbf{C}$$ . The intuition of $$\textbf{C}p$$ is that it is acceptable to assume p unless it is forced not to. This paper presents the minimal negated model semantics, a new semantics of ALP, where the minimal negated set is defined to formalize this intuition. The assumptions in this set must be negated, and the ones outside are considered acceptable. This paper also discusses the relationship between the minimal negated model semantics and the stable model semantics. This new semantics provides new insight into assumable logic programming.
Layout Graphs, Random Walks and the t-Wise Independence of SPN Block Ciphers

We continue the study of t-wise independence of substitution-permutation networks (SPNs) initiated by the recent work of Liu, Tessaro, and Vaikuntanathan (CRYPTO 2021). Our key technical result shows that when the S-boxes are randomly and independently chosen and kept secret, an r-round SPN with input length $$n = b \cdot k$$ is $$2^{-\varTheta (n)}$$ -close to t-wise independent within $$r = O(\min \{k, \log t\})$$ rounds for any t almost as large as $$2^{b/2}$$ . Here, b is the input length of the S-box and we assume that the underlying mixing achieves maximum branch number. We also analyze the special case of AES parameters (with random S-boxes), and show it is $$2^{-128}$$ -close to pairwise independent in 7 rounds. Central to our result is the analysis of a random walk on what we call the layout graph, a combinatorial abstraction that captures equality and inequality constraints among multiple SPN evaluations. We use our technical result to show concrete security bounds for SPNs with actual block cipher parameters and small-input S -boxes. (This is in contrast to the large body of results on ideal-model analyses of SPNs.) For example, for the censored-AES block cipher, namely AES with most of the mixing layers removed, we show that 192 rounds suffice to attain $$2^{-128}$$ -closeness to pairwise independence. The prior such result for AES (Liu, Tessaro and Vaikuntanathan, CRYPTO 2021) required more than 9000 rounds.
A Hypergraph Augmented and Information Supplementary Network for Session-Based Recommendation

Predicting the next interaction from a session without long-term historical data is challenging in session-based recommendation. Traditional approaches based on hypergraph modeling treat all items in sessions as interactions at the same time, producing hypergraphs that lose sequential information, which are susceptible to the interference of noise in long sessions. Besides, the way in which the information interacts through hyperedges in hypergraph convolution will lead to item embeddings lacking of global information. To address these issues, we propose a Hypergraph Augmented and Information Supplementary Network (HAISN), where the global graph self-supervised learning (GGSL) channel is designed to provide global and sequential information to the hypergraph. The Hypergraph augmented learning (HAL) channel is devised to supplement the hypergraph with filtered session information. Self-supervised learning is used to provide the information of the GGSL and HAL channels for the hypergraph convolution channel, improving the model effectively. Extensive experiments validate the effectiveness of HAISN.
Live-Stream Identification Based on Reasoning Network with Core Traffic Set

The proportion of live-stream traffic in network traffic has been boosting rapidly in recent years, which brings severe challenges to network management. Particularly, some game lives transmit harmful information, which poses great potential harm to adolescents. Therefore, how to effectively identify live-stream traffic becomes an urgent issue to be solved. However, traditional works based on machine learning (ML) methods do not consider many important causalities between flows and video in a live-stream session, lacking interpretability, which inspires us to propose a novel reasoning method to identify different live-stream scenarios. Firstly, we first propose a new technical concept namely core traffic set to contain the most important and significant flows in a live-stream session, and then analyzed the relationship between video flow and other related flows to construct the core traffic set for each session. Then the features related to the live-stream content will be extracted, and a Live-Stream Reasoning Network (LSRN) is designed to infer the corresponding type of live-stream. To evaluate the effectiveness of the proposed approach, a set of experiments are conducted on the dataset collected from the three platforms. In addition, compared with state-of-the-art (SOTA) methods and ensemble classifiers, the results also show that LSRN can significantly contribute to identifying the live video.
Dynamic Feed-Forward LSTM

We address the insufficient hidden states capabilities and single-direction feeding flaws of existing LSTM caused by its horizontal recurrent steps. To this end, we propose the Dynamic Feed-Forward LSTM (D-LSTM). Specifically, our D-LSTM first expands the capabilities of hidden states by assigning an exclusive state vector to each word. Then, the Dynamic Additive Attention (DAA) method is utilized to adaptively compress local context words into a fixed size vector. Last, a vertical feed-forward process is proposed to search context relations by filtering informative features in the compressed context vector and updating hidden states. With the help of exclusive hidden states, each word can preserve its most correlated context features and hidden states do not interfere with each other. By setting an appropriate context window size for DAA and stacking multiple such layers, the context scope can be gradually expanded from a central word to both sides and achieve the whole sentence at the top layer. Furthermore, the D-LSTM module is compatible with parallel computing and amenable to training via back-propagation for its vertical prorogation. Experimental results on both classification and sequence tagging datasets insist that our models achieve competitive performance compared to existing LSTMs.
Lattice-Based Succinct Arguments for NP with Polylogarithmic-Time Verification

Succinct arguments that rely on the Merkle-tree paradigm introduced by Kilian (STOC 92) suffer from larger proof sizes in practice due to the use of generic cryptographic primitives. In contrast, succinct arguments with the smallest proof sizes in practice exploit homomorphic commitments. However these latter are quantum insecure, unlike succinct arguments based on the Merkle-tree paradigm. A recent line of works seeks to address this limitation, by constructing quantum-safe succinct arguments that exploit lattice-based commitments. The eventual goal is smaller proof sizes than those achieved via the Merkle-tree paradigm. Alas, known constructions lack succinct verification. In this paper, we construct the first interactive argument system for NP with succinct verification that, departing from the Merkle-tree paradigm, exploits the homomorphic properties of lattice-based commitments. For an arithmetic circuit with N gates, our construction achieves verification time $$\textsf{polylog}(N)$$ based on the hardness of the Ring Short-Integer-Solution (RSIS) problem. The core technique in our construction is a delegation protocol built from commitment schemes based on leveled bilinear modules, a new notion that we deem of independent interest. We show that leveled bilinear modules can be realized from pre-quantum and from post-quantum cryptographic assumptions.
Machine Learning-Based Polarization Drift Compensation for High Speed DV-QKD Homodyne Receiver

Discrete variables quantum key distribution (DV-QKD), with its well-studied and scrutinized BB84 protocol, benefits from being very attractive for highly secure communications. However, current detection schemes rely on the use of InGaAs SPDAs, which limits not only its use in high temperature environments, but also high secure communication rates. A possible approach is the use of coherent homodyne detection schemes for polarization encoding based DV-QKD combined. To deploy polarization encoding DV-QKD over standard optical fiber high speed networks, the polarization drift suffered due to birefringence over the channel must be compensated. In this work, we use a machine learning (ML) polarization tracking and compensation algorithm combined with a coherent homodyne receiver, thus allowing the deployment of high-speed polarization encoding based DV-QKD in standard optical fibers. The ML-algorithm predicts the SOP evolution keeping the error rate below 1 %. In this way, the overhead to polarization monitoring is reduced leading to a secure key exchange rate (SKR) of 79 Mbps for a communication over 40 km optical fiber.
Silicon Ring Resonator with Phase-Change Material as a Plastic Dynamical Node for Scalable All-Optical Neural Networks with Synaptic Plasticity

Synaptic plasticity, that is the ability of connections in neural networks to strengthen or weaken depending on their input, is a fundamental component of learning and memory in biological brains. We present a numerical and experimental investigation of an integrated photonic plastic node, consisting of a silicon ring resonator enhanced by phase-change materials (GST). This all-optical device is capable of dynamical nonlinear behaviour, multi-scale volatile memory, non-volatile memory and multi-wavelength operations. We propose its employment as a building block in scalable all-optical dynamical neural networks that can adapt to their input via synaptic plasticity.
Entangled Pairs of Photons for Squeezed Light: Generation and Application

We discuss methods of generation of quantum entangled pairs of photons and present results obtained by non-linear process of four way mixing (FWM) in alkali atoms. Two-mode squeezing, known also as relative amplitude squeezing, by a pair of photons generated by FWM has been thoroughly investigated because of important applications of such paired photons. We show strong intensity squeezing in potassium vapour and also dependence of IDS on FWM parameters. Experimental results are compared with calculated IDS by the model of operators. Examples of application of twin photons for quantum enhanced sensing and quantum imaging and microscopy will be given.
Classification of Thermal Images for Human-Machine Differentiation in Human-Robot Collaboration Using Convolutional Neural Networks

Differentiation between human and non-human objects can increase efficiency of human-robot collaborative applications. This paper proposes to use convolutional neural networks for classifying objects in robotic applications. The body temperature of human beings is used to classify humans and to estimate the distance to the sensor. Using image classification with convolutional neural networks it is possible to detect humans in the surroundings of a robot up to five meters distance with low-cost and low-weight thermal cameras. Using transfer learning technique we trained the GoogLeNet and MobilenetV2. Results show accuracies of 99.48 % and 99.06 % respectively.
Developing a Novel Formulation for Team-Forming Problem of Coverage Problems with a Group of Heterogeneous Mobile Robots: Preliminary Results

Robotic platforms have been involved in our daily life more and more. Lately, this involvement has evolved to deploy a group of robots to do certain tasks such as floor cleaning, search-and-rescue missions, mine cleaning, and similar others. One of the main challenges that arise in such deployments is covering an area (or visiting the whole area) known as a coverage problem. In this paper, we present an approach for a coverage problem with a group of heterogeneous robots. Our approach is motivated by submapping approaches to alleviate the computational cost. Specifically, We discuss the problem of forming robot teams for covering the sub-maps for a given set of robots with different capacities in terms of battery consumption and velocity. We present a teamforming formulation as an optimization problem. We provide its efficiency via preliminary results using simulated data and the obtained results show promising results leaving room for further improvements.
Calibration of a Hand Exoskeleton Motion Capture Device using 3D Magnetic Sensors

The paper proposes a calibration method to estimate calibration parameters of sensors and mechanisms simultaneously for a hand exoskeleton motion capture device that can be worn on VR user's hands. The device measures finger movements and uses the information to control the movement of avatar's hand. A dedicated calibration jig is developed covering finger workspace to collect data for calibration and Levenberg-Marquardt algorithm is adopted for optimization. Finally, it is shown 3D positional errors of fingertips are reduced to around 2 mm at a minimum. This calibration method is expected to be useful in XR applications and telepresence robotics.
Secure and Lightweight Vehicular Privacy Preservation Scheme under Fog Computing-Based IoVs

As a prosperous technique, Internet of vehicles (IoVs) brings many conveniences for people's daily life, but there are some thorny secure issues that prevent IoVs from further deploying. Many schemes are proposed to solve the secure issues, in which vehicular on-board units (OBUs) or road side units (RSUs) preloaded entire vehicular real identities, which are used for authentication. However, OBUs or RSUs may be compromised, in other word, these existing schemes have their limitations to preserve vehicular identity privacy. Furthermore, in some schemes, the confidentiality of vehicular messages is not considered, clear-text vehicular messages may also disclose vehicular identity privacy. Therefore, we propose a secure and lightweight vehicular privacy preservation scheme for IoVs, in which fog servers use partial vehicular identity information to authenticate their legitimacy so as not to disclose vehicular identity privacy; lightweight and outsourcing decrypting attribute-based encryption methods are employed to achieve access control on sensitive vehicular messages and reduce resource-limited vehicular computation costs; moreover, an innovative conditional identity privacy preservation method is proposed to reveal the real identity of malicious vehicles without disclosing the real identity of honest vehicles even if there are some compromised fog servers or malicious vehicles. Experiments show our scheme is efficient.
ACF: An Adaptive Compression Framework for Multimodal Network in Embedded Devices

The ubiquitous Internet-of-Things (IoT) devices generate vast amounts of multimodal data, and the deep multimodal fusion network (DMFN) is a promising technology for processing multimodal data. Deploying DMFNs locally on embedded IoT devices is a profitable way to provide privacy-preserving and robust sensing services. However, the current compression methods suffer from the following limitations: First, they are designed based on unimodal networks or specific model structures. Hence, it is hard to extend these methods to diverse DMFNs; Second, existing works never relate their efforts to disparate computational demands of multimodal data and modalities. Easy samples and redundant modalities consume the same computational resources as powerful modalities and complex samples. We propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ompression <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> ramework (ACF) for DMFNs to address those challenges. It enables input-dependent runtime compression locally on resource-constrained embedded devices. Specifically, we propose an offline model transformation module to upgrade the static network with two kinds of dynamic components to support online structural adjustment. Then we design a lightweight policy network to generate multi-granularity and data-dependent compression strategies for different model parts. Finally, we evaluate ACF on four DMFNs across three embedded platforms. Compared with the best results of the existing schemes, ACF obtains up to 2.61× latency reduction and 2.30× energy consumption reduction, with up to 3.57% accuracy improvement.