Abstract:In multi-party group conversations, identifying the reply-to relation between historical messages is an important task in the dialogue domain. Despite of previous efforts, two issues with respect to the data distribution still remained: First, short messages with sparse semantics make up a significant portion of the messages, which in turn restricts the learning potential of the models. Second, positive examples with reply-to relations are often much fewer than negative examples, resulting in data skewness during model training and hindering the model''s performance on positive examples. To address these two issues, this paper proposes an improved model based on a pre-trained language model. Our method first mitigates the issue of short messages by developing a dynamic inquiry window that enriches semantic modeling with comprehensive semantics. Then, it tackles the problem of positive example imbalance through position-driven optimization of positive example weights. Experimental results on the public benchmark show that our method improved model achieves a recall of 62.2% and a F-1 score of 59.4%, which are 15.7% and 8.5% higher than the average baseline model, respectively. The paper also constructs a new dataset collected from the Telegram platform, providing data support for future related research.

Abstract:Against the backdrop of Moore''s Law approaching its limit and the difficulty and surging cost of next-generation integrated circuit technologies, advanced substrate technology is an important carrier to support huge I/O enhancement as well as system integration in the field of advanced packaging, and is one of the core components in the post-Moore era. Currently, semi-additive process based on build-up film (BF) is one of the main ways to realize fine-pitch multilayer packaging substrates. In view of the increasingly prominent problem of signal integrity when electronic equipment operates in high-frequency and high-speed scenes, this paper deeply discusses the influence of physical property of BF materials and structural characteristics on signal transmission loss. Based on typical substrate structures such as microstrip lines and vias, the relationship between BF material parameters and signal transmission performance is studied by electrical simulation analysis system. It is found that in microstrip structure, the signal transmission loss increases with the increase of frequency, and this loss is closely related to the dielectric loss factor of BF material. However, in the via structure, the dielectric constant of BF material has a significant influence on the equivalent capacitance and impedance extreme value, and then affects the impedance mismatch. Although the characteristics of BF material have some influence on impedance mismatch, the design of via structure itself is still the main factor affecting impedance matching. In addition, the conductor loss caused by conductor skin effect increases with the increase of copper foil roughness at high frequency, which provides an important reference for the quality control of copper foil in the manufacturing process of packaging substrate. This study reveals the influence mechanism of BF material and structural characteristics on signal transmission loss, which provides a theoretical basis for the design and optimization of BF material with improved physical properties for packaging substrate.

Abstract:In the field of encrypted mobile application traffic classification, traditional methods classify traffic based on the characteristics of bidirectional traffic. However, in actual scenarios, asymmetric routing will cause remote monitors to only obtain unidirectional traffic, which will reduce the accuracy of traditional methods. Therefore, this paper designs an encrypted mobile application traffic classification method using only one-way traffic characteristics. Since downlink traffic contains more information than uplink traffic, this paper chooses to analyze the payload of downlink traffic. Due to the temporal and spatial correlation of mobile application traffic, a bidirectional long short-term memory network is proposed to capture the temporal correlation of data streams, a convolutional neural network is used to learn the spatial correlation of features, and an attention layer is introduced to focus on important features to further improve the recognition accuracy. Compared with the previous methods, this method has a wider range of use, can be applied to both unidirectional and bidirectional traffic scenarios, and uses fewer features to obtain higher accuracy.

Abstract:Data provenance technology is capable of recording and tracking the origins of sensitive documents to prevent their leakage. Traditional network path tracing methods are ineffective in tracking offline documents, and key tracing for encrypted files does not ensure reliable provenance for shared files. Existing techniques such as annotation, reverse querying, and data watermarking often require user involvement and are implemented at the application layer, resulting in inadequate security, lack of transparency and flexibility, and insufficient overall system scalability. This paper introduces an innovative script-based dynamic fingerprint provenance architecture that utilizes modifications to the Linux kernel to achieve foundational provenance, enhancing the security and transparency of document tracing. The fingerprint tracking algorithm is implemented through user scripts, improving the flexibility and effectiveness of document provenance. Additionally, the fingerprint-driven algorithm is designed to meet the demands of multi-load sharing, ensuring efficient and scalable document sharing. Upon verification, this architecture has a minimal impact on the operating system and exhibits excellent scalability. In scenarios involving single or multiple load sharing, the fingerprint-driven algorithm demonstrates transparency, real-time performance, and efficiency.

Abstract:In this work, a new paradigm of visual language modeling is introduced in ophthalmic image disease recognition. And a multi-disease recognition algorithm based on a pre-trained model of contrasting language images is proposed. First, a new multi-labeled fundus image dataset MDFCD8 containing 8 categories is constructed based on several publicly available fundus image datasets. Then, the generative artificial intelligence GPT-4 is utilized to generate expert knowledge describing the fine-grained pathological features of fundus images, which solves the problem of the lack of text labels in fundus image datasets. The experimental results showed that, the proposed method outperforms the traditional convolutional neural network and Transformer network by 4.8% and 3.2%, respectively. This study also conducted ablation experiments on each module to validate the effectiveness of the method, and also demonstrated the potential of visual language modeling in ophthalmic disease research.

Abstract:Adenoid hypertrophy (AH) is a key contributor to pediatric obstructive sleep apnea syndrome (OSAS). Physicians rely on nasopharyngeal endoscopy to identify AH and the obstruction of adenoid to the airway. However, due to the limitations of 2D endoscope images, physicians have to infer the 3D structure of the adenoid region, which heavily relies on their expertise and the angle at which the adenoids are observed. The adenoid area is composed of mucosal tissue covered by nasal secretions, which may cause strong reflectivity, sparse features, smooth scenes, and blurred images. Based on these unique characteristics of the adenoids, this paper introduces a multi-view stereo algorithm based on endoscopic image sequences of the adenoid nasopharyngeal cavity. The algorithm employs multi-view stereo to first estimate a depth map corresponding to the images. Subsequently, it utilizes mesh surfaces to fit the rough depth information in the depth space, resulting in smooth and refined depth maps. This leads to a dense and precise reconstruction of the adenoid region. Both synthetic and real experimental results demonstrate that the algorithm can achieve accurate, dense, and smooth reconstruction of the adenoid area, surpassing the existing reconstruction algorithms significantly.

Abstract:This paper presents a design of single-joint biomimetic robotic fish with compact structure and high swimming efficiency. It allows for convenient disassembly and assembly of pectoral fins, pelvic fins, and caudal fins. The influence of pectoral and pelvic fins on swimming performance was studied via underwater experiments. In the prototype swimming tests, a "binocular vision system" for tracking and recording the motion of the robotic fish was constructed using a high-speed camera and a flat mirror. It enabled tracking and recording of the three-dimensional position information of two marked points on the foremost end of the fish head and above its center of mass. This system provided data support for the quantitative analysis of the swimming performance, posture changes, and head stability of the robotic fish. The results indicated that the robotic fish have good performance in linear propulsion and turning. In the stability experiments, the head stability of the robotic fish equipped with pectoral fins and pelvic fins is better during low-frequency swimming. But no advantage is shown during high-frequency swimming, which is consistent with the phenomenon of various fins of fish in the natural environment being close to the body during high-frequency swimming except for the caudal fin.

Abstract:LOGO is a new distributed computing framework using a Non-MapReduce computing paradigm. Under the LOGO framework, big data distributed computing is completed in two steps. The LO operation runs a serial algorithm in a number of nodes or virtual machines to process independently the random sample data blocks, generating local results. The GO operation uploads all local results to the master node and integrate them to obtain the approximate result of the big data set. The LOGO computing framework eliminates data communication between nodes during iterations of the algorithm, greatly improving computing efficiency, reducing memory requirements, and enhancing data scalability. This article proposes a new distributed machine learning algorithm library RSP-LOGOML under the LOGO computing framework. A new distributed computing is divided into two parts: the serial algorithm executed by the LO operation and the ensemble algorithm executed in the GO operation. The LO operation can directly execute existing serial machine learning algorithms without the need to rewrite them according to MapReduce. The GO operation executes ensemble algorithms of different kinds depending on the ensemble tasks. In this article, the principle of LOGO distributed computing is introduced first, followed by the algorithm library structure, the method for packaging existing serial algorithms and the ensemble strategy. Finally, implementation in Spark, App development, and the results of performance tests for various algorithms are demonstrated.

Abstract:The ability to mimic the microenvironment of the human body through fabrication of scaffolds itself a great achievement in the biomedical field. However, the search for the ideal scaffold is still in its infant stage and there are significant challenges to overcome. In the modern era, scientific communities are more attracted to natural substances due to their excess biological ability, low cost, biodegradability, and lesser toxic than synthetic lab made products. Chitosan is a well-known polysaccharide that has recently grabbed high amount of attention for its biological activities, especially in 3D bone tissue engineering (BTE). Chitosan greatly matches with the native tissues and thus stands out as a popular candidate for bioprinting. This review focuses on the potential of chitosan based scaffolds advancement and the drawbacks in bone treatment. Chitosan-based nanocomposites have exhibited strong mechanical strength, water-trapping ability, cellular interaction, and biodegradability characteristics. Chitosan derivatives have also encouraged and provided different routes of treatment and enhanced biological activities. 3D tailored bioprinting have opened new doors to design and manufacture scaffolds of biological, mechanical, and topographical properties.

Abstract:In today''s digital age, Nginx has emerged as the most prevalent web application server on Linux systems, securing the top position in market share. Given its critical role in ensuring the quality of service for users, optimizing the performance of Nginx servers is important. Despite the widespread deployment of Nginx servers across the two main hardware architectures, X86 and ARM, a comparative analysis of performance tuning on these architectures remains unexplored. This study aims to bridge this gap by employing automatic system parameter tuning on Nginx across these architectures, revealing the significant difference. When handling dynamic requests, the optimized performance of Nginx on X86 architecture significantly outperforms that of the ARM architecture. As a result, the optimized performance of Nginx on X86 architecture achieves a P99 latency of 515 milliseconds, which is performance improvement of 287% than that of the ARM architecture. Conversely, when processing static requests, the ARM architecture demonstrates superior performance, with a P99 latency of 220 milliseconds, resulting in a performance increase of 60% than that of X86 architecture. These findings highlight the distinct advantages of X86 and ARM architectures in handling different types of loads. It shows the significant impact of hardware architecture on optimizing Nginx’s performance. Therefore, to optimize the performance of Nginx web server, system administrators must consider the performance differences between static and dynamic requests of Nginx and the unique iterative efficiency over different hardware architectures.