Multiple Sources for Security: Seeking Online Safety Information and their Influence on Coping Self-efficacy and Protection Behavior Habits

Internet users face threats of increasing complexity and severity. To protect themselves they rely on sources for online safety information. These sources may either build up, or undermine, the coping self-efficacy and motivation needed to protect oneself. A survey of 800 subjects asked about which sources they relied on for information about online safety: media, work, school, friends and family, and specialized web sites. Individuals who said they had no comprehensive source for information reported the lowest levels of both coping self-efficacy (b= -0.609, p\u3c 0.001) and protection habit strength (b= -0.900, p\u3c 0.001). On the other hand, those who had an affiliation of school, work and specialized web sites had a positive relationship with both coping self-efficacy (b= 0.517, p\u3c 0.05) and protection habit strength (b= 0.692, p\u3c 0.05). Results suggest that some information affiliation networks are correlated with higher coping self-efficacy and stronger protection habits

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

The Study of Rock Body Damage Constitutive Model on Refracturing

In order to characterize the mechanical behavior of rock body damage evaluation and forming multiple fractures, in this paper in multiple fracturing , we have established rock body damage evaluation constitutive model, and given the point that the rock can bear secondary damage in multiple fracturing. Established the secondary damage evaluation model, and obtained the method for calculating the parameter of the crack in multiple fracturing. We have verified the model by a oil well in Jilin oilfield, the result has well anastomosis with the actual engineering.Key words: Multiple fracturing; Damage evaluation; Secondary damag

Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.Comment: 10 pages, accepted as a full paper in KDD 202

GraphScope Flex: LEGO-like Graph Computing Stack

Graph computing has become increasingly crucial in processing large-scale graph data, with numerous systems developed for this purpose. Two years ago, we introduced GraphScope as a system addressing a wide array of graph computing needs, including graph traversal, analytics, and learning in one system. Since its inception, GraphScope has achieved significant technological advancements and gained widespread adoption across various industries. However, one key lesson from this journey has been understanding the limitations of a "one-size-fits-all" approach, especially when dealing with the diversity of programming interfaces, applications, and data storage formats in graph computing. In response to these challenges, we present GraphScope Flex, the next iteration of GraphScope. GraphScope Flex is designed to be both resource-efficient and cost-effective, while also providing flexibility and user-friendliness through its LEGO-like modularity. This paper explores the architectural innovations and fundamental design principles of GraphScope Flex, all of which are direct outcomes of the lessons learned during our ongoing development process. We validate the adaptability and efficiency of GraphScope Flex with extensive evaluations on synthetic and real-world datasets. The results show that GraphScope Flex achieves 2.4X throughput and up to 55.7X speedup over other systems on the LDBC Social Network and Graphalytics benchmarks, respectively. Furthermore, GraphScope Flex accomplishes up to a 2,400X performance gain in real-world applications, demonstrating its proficiency across a wide range of graph computing scenarios with increased effectiveness

Antitumor activity and safety of camrelizumab combined with apatinib in patients with relapsed or refractory peripheral T-cell lymphoma: An open-label, multicenter, phase II study

IntroductionThe treatment for relapsed/refractory peripheral T-cell lymphoma (r/r PTCL) is suboptimal. This open-label, multicenter, single-arm study aimed to investigate the antitumor activity and safety of camrelizumab (a PD-1 blockade) plus apatinib (an antiangiogenic agent) for patients with r/r PTCL.MethodsEligible patients with r/r PTCL were enrolled and received camrelizumab 200 mg intravenously every 2 weeks and apatinib 500 or 250 mg orally once daily, 4 weeks as a cycle. The primary endpoint was overall response rate (ORR).ResultsA total of 20 patients were enrolled and received study medications in the study, with a median number of prior treatment line of 3 (range 1-6). At the cutoff date of March 4, 2022, the median follow-up was 27.2 months (range: 0.5-39.9), and three patients remained on treatment. Six patients had early discontinuation without tumor response evaluation. For all patients, the ORR was 30% (6/20) (95% confidence interval [CI], 11.9% to 54.3%), with two patients (10%) achieving complete response. The median progression-free survival (PFS) and median overall survival for all patients were 5.6 months (95% CI, 1.8 to not reached) and 16.7 months (95% CI, 2.8 to not reached), respectively. Patients with PD-L1 expression ≥50% (3 patients) had a numerically higher ORR and longer median PFS than those with PD-L1 expression < 50% (5 patients). The most commonly reported grade 3 or higher adverse events were hyperlipidemia (15%), hypokalemia (15%) and anemia (15%). No treatment-related deaths occurred.DiscussionIn this study, PD-1 inhibitors plus low-dose antiangiogenic drugs presented preliminary antitumor activity and manageable toxicity in patients with r/r PTCL