Reviewerly/exHarmony huggingface dataset:
Securing InfiniBand Networks with the Bluefield-2 Data Processing Unit

Interest in securing InfiniBand networks with encryption is growing. However, the performance benefit realized by InfiniBand’s use of Direct Memory Access (DMA) to bypass the kernel and avoid intervention from host Central Processing Units (CPUs) is at odds with IP datagram encryption techniques. Encryption forces data through the CPU before transmission and decryption, incurring multiple clock cycles. The Bluefield-2 Data Processing Unit (DPU) is Nvidia-Mellanox’s latest system on chip that combines a high-performance, programmable processor, network interface card (NIC), and flexible hardware accelerators. This research characterizes the Bluefield-2’s capability to accelerate IPsec encryption in hardware. Results show that the Bluefield-2’s hardware accelerators are capable of supporting a secure IPsec tunnel with a throughput of nearly 16 Gb/s. Offloading IPsec encryption operations to the hardware accelerators on the Bluefield-2 is a promising method for adding confidentiality, integrity, and authentication to InfiniBand networks.
Exploration on APTs in Biocybersecurity and Cyberbiosecurity

Novel and complex digital threats that are increasingly interwoven with means and products of biology that can affect society. Much work in Biocybersecurity/Cyberbiosecurity (BCS/CBS) discuss vulnerabilities, but few deeply address malicious actor varieties as attacks at this intersection are new. The path to those attacks remains mostly theoretical, presenting considerable difficulty to accomplish in practical scenarios. In terms of advanced persistent threats (APTs) this of course needs to change as biomanufacturing facilities are at risk, considering Covid-19 and other potential pandemics. Further attacks are not out of reach and thus we must start to imagine how BCS APTs may appear. This paper in progress aims to open discussion regarding the definition of the concept BCS/CBS APTs and their implications, as well as create call to action for increased attention.
“Depends on Who I'm Writing To”—The Influence of Addressees and Personality Traits on the Use of Emoji and Emoticons, and Related Implications for Forensic Authorship Analysis

Converging towards or diverging from an interlocutor's speaking style (i.e., linguistic accommodation) has been investigated in many previous studies and is a highly relevant phenomenon in forensic authorship analysis. Accommodation has also been linked to personality traits, but there is still a lack of investigations of accommodation in computer-mediated communication. The present study thus aims at examining in how far emoji use is subject to accommodation in casual online interactions, and how reliable both emoji and emoticons are as markers of authorship. Further, this study is interested in finding out about connections between both emoji and emoticon use and the Big Five personality traits of agreeableness, emotional stability, conscientiousness, openness to experiences, and extraversion. The results of the analysis show that the frequency of emoji use is indeed strongly influenced by conversation partners, and that both emoji and emoticon use correlate particularly with extraversion and agreeableness. Despite the influence of conversation partners on emoji use, it can further be shown that emoji and emoticons remain valuable markers of authorship to different degrees.
Robust Quantum State Tomography Method for Quantum Sensing

Reliable and efficient reconstruction of pure quantum states under the processing of noisy measurement data is a vital tool in fundamental and applied quantum information sciences owing to communication, sensing, and computing. Specifically, the purity of such reconstructed quantum systems is crucial in surpassing the classical shot-noise limit and achieving the Heisenberg limit, regarding the achievable precision in quantum sensing. However, the noisy reconstruction of such resourceful sensing probes limits the quantum advantage in precise quantum sensing. For this, we formulate a pure quantum state reconstruction method through eigenvalue decomposition. We show that the proposed method is robust against the depolarizing noise; it remains unaffected under high strength white noise and achieves quantum state reconstruction accuracy similar to the noiseless case.
Blockchain Technology and the Future of Construction Industry in the Arab region :Applications, Challenges, and Future Opportunities

The construction industry sector is one of the oldest sectors in the world and plays a major role in the development and advancement of societies. However, the construction industry suffers from low production and efficiency compared to other sectors for several reasons, including the different interests of the parties involved in construction projects and the fear of some of keeping pace with modern technological developments, in addition to the lack of confidence among the participants in the project, which are among the most important factors that reduce efficiency and productivity. In 2008, a technology emerged with the ability to overcome many of the challenges facing the construction industry, namely, the blockchain technology, which is associated with the famous cryptocurrency bitcoin. Its main purpose is to prevent changes to digital documents and preserve the data stored in them to prevent it from being modified. This technology has applications in many fields. The construction sector, especially in the Arab region, suffers from a lack of knowledge of the potential of this technology to help the construction industry in the digital age. The main objectives of this study were to explore the role of blockchain technology in shaping the future of the construction industry and to determine what this technology could contribute to the construction sector, especially in combination with building information modelling (BIM) technology. The researchers assumed that the adoption of blockchain technology in the construction industry would be a solution to a large portion of the current problems of the industry, which have slowed the adoption of BIM technology. Literature on the concept of blockchain technology and its applications to the construction industry was reviewed. Semi-structured interviews with Arab experts and specialists were conducted to collect information and qualitative data about the use of blockchain technology in the construction industry, with the aim of conducting a strength– weaknesses–opportunities–threats (SWOT) analysis. The views and experiences of the experts interviewed with respect to the potential advantages of and opportunities offered by the integration of blockchain technology and BIM technology in the construction industry in the Arab region were sought, in order to propose a conceptual framework to facilitate the automation of the processes and the stages of design, tendering, construction, maintenance, and effective management of the entire life cycle of construction projects.
Spilled load detection based on lightweight YOLOv4 trained with easily accessible synthetic dataset

The automated detection of spilled loads on the highway can help quick finding of offending vehicles and prevent traffic congestion. A real-time spilled loads detection algorithm based on the improved You Only Look Once v4 (YOLOv4) is proposed in this paper. Image synthesis method is used to manually generate dataset to solve the problem of insufficient spilled loads images. Six categories of spilled loads are divided into 35,468 samples as a dataset (80% for training and 20% for testing). K-means clustering and Convolutional Block Attention Module (CBAM) were introduced to YOLOv4 Channel pruning was carried out to reduce the computational complexity and resource consumption of the model. The proposed model, of which average precision=98.3%, recall= 0.962, precision=0.98, performs better than other 5 types classifiers and is applied to estimate spilled loads, where the model size and GFLOPs is 134.535 MB and 70 respectively. Compared with the original model, the reduction is 47.6% and 34%, saving computing resources and speeding up the detection speed on the premise of ensuring the detection accuracy. This paper paves a new way for automated spilled load detection with deep learning method, improving the efficiency and accuracy of spilled load detection.
Housing Rental Scheme Based on Redactable Blockchain

With the increase of the housing population, it is very important for renters to find credible housing information through rental platforms. However, due to the imperfection of relevant laws and insufficient supervision of the housing rental market, problems have emerged one after another. In this article, we propose a housing rental scheme based on redactable blockchain, introducing a trusted third party to verify the homeowner rental listings, and fundamentally preventing false listings from flowing into the housing rental market. Specifically, a trusted third party similar to the housing authority will issue a verifiable claim for the homeowners listing, and then the accounting node of the consortium blockchain will publish the verified listing information on the blockchain so as tenants to make inquiries according to their needs. At the same time, we use the new chameleon hash to build a redactable blockchain. After the homeowner proposes to change the housing price or housing information, the content of the historical block is modified as required. Thus, to a certain extent, the changes in housing prices can be monitored to curb chaos in the housing rental market.
The Optimization Function of Computer Image Technology in Processing Oil Painting Creation

In terms of processing methods, machine learning mainly takes the network as the core and uses the network as a medium to provide services such as computing and delivery. The “cloud” is mainly composed of computers and servers, and the Internet is used to provide network services. With the continuous progress and development of computer technology, digital image processing technology based on machine learning is widely used in production and life. Machine learning uses its own storage and computing capabilities to make the development space of image processing technology broader and effectively realize the rational allocation of resources. This article mainly analyzes the basic knowledge of machine learning, the construction of image processing cloud platforms, and the image fusion system. Then, we aim to optimize the oil painting creation. Through a simple explanation of the basic connotation of images, it is clear that digital image technology and oil painting creation are closely related and complementary. The impact of education and focusing on the application of computer graphics technology to some of the techniques often used in oil painting creation makes the created works more beautiful and artistic and puts forward the application of image processing technology and computer-aided technology in oil painting creation.
SEIHAI: A Sample-Efficient Hierarchical AI for the MineRL Competition

The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex ObtainDiamond task with sparse rewards. To address the challenge, in this paper, we present SEIHAI, a Sample-efficient Hierarchical AI, that fully takes advantage of the human demonstrations and the task structure. Specifically, we split the task into several sequentially dependent subtasks, and train a suitable agent for each subtask using reinforcement learning and imitation learning. We further design a scheduler to select different agents for different subtasks automatically. SEIHAI takes the first place in the preliminary and final of the NeurIPS-2020 MineRL competition.
Entanglement dynamics of an open moving-biparticle system driven by classical-field

In this work, the entanglement dynamics of a moving-biparticle system driven by an external classical field are investigated, where the moving-biparticle system is coupled with a zero temperature common environment. The analytical expressions of the density operator and the entanglement can be obtained by using the dressed-state basis when the total excitation number is one. We also discuss in detail the effects of different parameters on the entanglement dynamics. The results show that the classical driving can not only protect the entanglement, but also effectively eliminate the influence of the qubit velocity and the detuning on the quantum entanglement.
Diffusion Tensor Imaging Observation of Frontal Lobe Multidirectional Transcranial Direct Current Stimulation in Stroke Patients with Memory Impairment

Stroke is a group of diseases caused by the sudden rupture or blockage of blood vessels in the brain that prevent blood from flowing into the brain, resulting in brain tissue damage and dysfunction. Stroke has the characteristics of high morbidity, high disability, and high mortality. To investigate the effect of multidirectional transcranial direct current stimulation (tDCS) of the prefrontal lobe in stroke memory disorder. We evaluated 60 patients with poststroke memory impairment who underwent magnetic resonance diffusion tensor imaging (DTI) during their admission to our hospital between January 2018 and December 2020. The patients were divided into the prefrontal group (n = 15), dorsolateral group (n = 15), prefrontal + dorsolateral group (n = 15), and pseudostimulation group (n = 15). Assessments using the Rivermead Behavioral Memory Test (RBMT), Montreal Cognitive Assessment Scale (MoCA), Lovingston Occupational Therapy Cognitive Scale (LOTCA), and frontal lobe fractional anisotropy (FA) were performed before and after treatment. The RBMT, MoCA, and LOTCA scores in the prefrontal + dorsolateral group were significantly higher than those in the dorsolateral, prefrontal, and sham groups (all <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The posttreatment FA value of the frontal lobe was significantly higher in the prefrontal + dorsolateral group than in the dorsolateral, prefrontal, and sham stimulation groups (all <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The FA value of the frontal lobe was significantly lower in patients with severe memory impairment than in patients with mild-moderate memory impairment ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The area under the receiver operating characteristic curve was 0.801 (95% CI: 0.678–0.925, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ), and the optimal cut-off value was 0.34, with a sensitivity and specificity of 81.60% and 72.70%, respectively. Prefrontal lobe + dorsolateral tDCS is beneficial in the treatment of post-stroke memory impairment. The DTI FA value can be useful in determining the degree of memory impairment.
Key Radar Signal Sorting and Recognition Method Based on Clustering Combined PRI transform Algorithm

In this paper, we investigate the problem of key radar signal sorting and recognition in electronic intelligence (ELINT). Our major contribution is the development of a combined approach based on clustering and PRI transform algorithm, to solve the problem that the traditional methods based on Pulse Description Words (PDW) were not exclusively targeted at tiny particular signals and, less time-efficient. We achieve this in three steps: firstly, PDW presorting is carried out by the DBSCAN clustering algorithm, then, PRI estimates of each cluster are obtained by the PRI transformation algorithm, finally, by judging the matching between various PRI estimates and key targets, it is determined whether the current signal contains key target signals or not. Simulation results show that the proposed method should improve the time efficiency of key signal recognition and deal with the complex signal environment with noise interference and overlapping signals.
Persuasion or Co-creation? Social Identity Threat and the Mechanisms of Deliberative Transformation

Deliberation’s effectiveness as a method of problem solving and democratic decision making is often seen as stemming from the persuasive power of the “forceless force” of argument to transform beliefs. However, because conflicts related to partisan polarization, conspiracy theories, and the COVID-19 pandemic often have deep connections to social identity, they may be difficult to resolve through a deliberative approach based on persuasion. Research shows that when the conclusions of an argument threaten participants’ social identities they are likely to engage in motivated reasoning, which inhibits the ability of any argument to induce belief change. In conflicts closely related to social identity a deliberative approach based around co-creation — such as Mary Parker Follett’s conception of integration — may be more productive than persuasion-based approaches. The contrast between these two approaches is illustrated in reference to contemporary conflicts between vaccine advocates and members of the “vaccine hesitancy and refusal” (VHR) community.
Graph kernels based on optimal node assignment

The success of kernel algorithms depends on the kernel it uses and hence the development of kernels for structured data like graphs is an active research field. We designed two graph kernels by making use of the optimal assignment kernel framework, which is a less explored graph framework compared to its counterpart, namely, R-convolution kernels. In our work, the bijection associated with the optimal assignment framework is defined between sets that consist of the nodes of the graph kernel arguments and hence we named the proposed kernels as optimal node assignment kernels (ONA). In ONA, the nodes of the given data are divided into groups named as neighbourhood sets on the basis of the labels generated by the Weisfeiler–Lehman (WL) test for graph isomorphism and a matrix representation is defined for them. A kernel is then defined over the domain that consists of the neighbourhood sets in terms of such matrices and an aggregate measure of those kernel values is used for defining ONA kernel. The validity of the proposed kernels is mathematically proved. The kernel calculation using the hierarchy structure associated with the optimal assignment framework is also discussed. The performance of the proposed ONA kernels was analysed using graph classification datasets and was found to be superior in comparison with the other state-of-the-art graph kernels.
Reinforcement learning for resource management in multi-tenant serverless platforms

Serverless Function-as-a-Service (FaaS) is an emerging cloud computing paradigm that frees application developers from infrastructure management tasks such as resource provisioning and scaling. To reduce the tail latency of functions and improve resource utilization, recent research has been focused on applying online learning algorithms such as reinforcement learning (RL) to manage resources. Compared to existing heuristics-based resource management approaches, RL-based approaches eliminate humans in the loop and avoid the painstaking generation of heuristics. In this paper, we show that the state-of-the-art single-agent RL algorithm (S-RL) suffers up to 4.6x higher function tail latency degradation on multi-tenant serverless FaaS platforms and is unable to converge during training. We then propose and implement a customized multi-agent RL algorithm based on Proximal Policy Optimization, i.e., multi-agent PPO (MA-PPO). We show that in multi-tenant environments, MA-PPO enables each agent to be trained until convergence and provides online performance comparable to S-RL in single-tenant cases with less than 10% degradation. Besides, MA-PPO provides a 4.4x improvement in S-RL performance (in terms of function tail latency) in multi-tenant cases.
A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the recognition accuracy; the network architecture is adjusted to accelerate the training convergence speed and improve the recognition accuracy. The experimental results show that the A-ResNet model achieves a top-1 accuracy improvement of about 2% compared with the traditional ResNet network. Image recognition is one of the core technologies of computer vision, but its application in the field of tea is relatively small, and tea recognition still relies on sensory review methods. A total of 1,713 images of eight common green teas were collected, and the modeling effects of different network depths and different optimization algorithms were explored from the perspectives of predictive ability, convergence speed, model size, and recognition equilibrium of recognition models.
High solubility of gold in H2S-H2O ± NaCl fluids at 100–200 MPa and 600–800 °C: A synthetic fluid inclusion study

Numerous previous experimental and theoretical studies of gold deposits in the Earth’s crust have shown that chloride and hydrosulfide are the most abundant ligands in the relevant hydrothermal fluids. However, most of these studies were performed with solutions under relatively oxidizing conditions in the presence of SO42-, HSO4-, and S3-. Reduced sulfur with a −2 valence (e.g., H2S, HS-, etc.) may play a key role in controlling redox conditions and promoting the migration of gold during the formation of epithermal and porphyry gold-copper deposits. To examine this possibility, we performed experiments in reduced H2S-bearing fluids to constrain the effects of temperature, pressure and NaCl concentration on Au solubility through the synthetic fluid inclusion method. Measured amounts of H2S and aqueous solutions (H2O ± NaCl) were loaded in an Au capsule containing a fractured quartz column, and fluid inclusions were formed through healing of these fractures at specified pressures (100 or 200 MPa) and temperatures (600, 700 or 800 °C) for 20 days. Thin sections with both sides polished were prepared from quenched quartz columns, and conventional microthermometric measurements were performed. Raman spectra were collected from all phases in the fluid inclusions from room temperature to 300 °C to identify sulfur species and potential Au-bearing complexes. In all fluid inclusions, S and H2Sn (n ≥ 1) species were identified, but SO2 and SO42-, HSO4- and S3- ions were not observed. Au concentrations in these fluid inclusions, which were analyzed using LA-ICP-MS with NaCl or RbCl reference standards, ranged from 91 to 3599 ppm; they were greatly affected by pressure and NaCl concentration but not obviously by temperature. H2Sn (n > 1) analogs have chemical properties similar to those of H2S, and their deprotonation products (HSn- and Sn2-) exhibit high affinities for Au in aqueous solution. Therefore, HSn-Au (or AuSn-) and Au-Cl could be the dominant species in H2S-bearing hydrothermal fluids and may play important roles during the dissolution and transport stages of Au in ore formation processes.