Secure Pick Up: Implicit Authentication When You Start Using the Smartphone

We propose Secure Pick Up (SPU), a convenient, lightweight, in-device, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background, our system implicitly observes users' phone pick-up movements, the way they bend their arms when they pick up a smartphone to interact with the device, to authenticate the users. Our SPU outperforms the state-of-the-art implicit authentication mechanisms in three main aspects: 1) SPU automatically learns the user's behavioral pattern without requiring a large amount of training data (especially those of other users) as previous methods did, making it more deployable. Towards this end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW) algorithm to effectively quantify similarities between users' pick-up movements; 2) SPU does not rely on a remote server for providing further computational power, making SPU efficient and usable even without network access; and 3) our system can adaptively update a user's authentication model to accommodate user's behavioral drift over time with negligible overhead. Through extensive experiments on real world datasets, we demonstrate that SPU can achieve authentication accuracy up to 96.3% with a very low latency of 2.4 milliseconds. It reduces the number of times a user has to do explicit authentication by 32.9%, while effectively defending against various attacks.Comment: Published on ACM Symposium on Access Control Models and Technologies (SACMAT) 201

Application of Natural Polysaccharides and Their Novel Dosage Forms in Gynecological Cancers: Therapeutic Implications From the Diversity Potential of Natural Compounds

Cancer is one of the most lethal diseases. Globally, the number of cancers is nearly 10 million per year. Gynecological cancers (for instance, ovarian, cervical, and endometrial), relying on hidden diseases, misdiagnoses, and high recurrence rates, have seriously affected women\u27s health. Traditional chemotherapy, hormone therapy, targeted therapy, and immunotherapy effectively improve the prognosis of gynecological cancer patients. However, with the emergence of adverse reactions and drug resistance, leading to the occurrence of complications and poor compliance of patients, we have to focus on the new treatment direction of gynecological cancers. Because of the potential effects of natural drugs in regulating immune function, protecting against oxidative damage, and improving the energy metabolism of the body, natural compounds represented by polysaccharides have also attracted extensive attention in recent years. More and more studies have shown that polysaccharides are effective in the treatment of various tumors and in reducing the burden of metastasis. In this review, we focus on the positive role of natural polysaccharides in the treatment of gynecologic cancer, the molecular mechanisms, and the available evidence, and discuss the potential use of new dosage forms derived from polysaccharides in gynecologic cancer. This study covers the most comprehensive discussion on applying natural polysaccharides and their novel preparations in gynecological cancers. By providing complete and valuable sources of information, we hope to promote more effective treatment solutions for clinical diagnosis and treatment of gynecological cancers

Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. To tackle the SSDA problem on graphs, a novel method called SemiGCL is proposed, which benefits from graph contrastive learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph's local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks

Lysosomes, Curcumin, and Anti-Tumor Effects: How Are They Linked?

Curcumin is a natural active ingredient from traditional Chinese medicine (TCM) that has multi-target characteristics to exert extensive pharmacological activities and thus has been applied in the treatment of various diseases such as cancer, cardiovascular diseases, nervous system, and autoimmune disorders. As an important class of membranous organelles in the intracellular membrane system, lysosomes are involved in biological processes such as programmed cell death, cell metabolism, and immune regulation, thus affecting tumor initiation and progression. It has been shown that curcumin can modulate lysosomal function through the aforementioned pathways, thereby affecting tumor proliferation, invasion, metastasis, drug resistance, and immune function. This review briefly elaborated the regulatory mechanisms of lysosome biogenesis and summarized curcumin-related studies with its anti-tumor effect, providing a reference for the clinical application of curcumin and anti-tumor research targeting lysosomes

Emerging Current Trends and Research Focus Related to Pancreatic Cancer Metabolism: A Bibliometric and Visualized Analysis

BACKGROUND: As a malignant digestive system tumor, pancreatic cancer has unique metabolic characteristics. In recent years, the study of pancreatic cancer metabolism is in full swing, which provides a new direction for the treatment of pancreatic cancer patients. However, there is no systematic report of pancreatic cancer metabolism. In this paper, bibliometrics and visualization methods were used to analyze the number of publications, countries/regions, authors, institutions, journals, co-cited references, and keywords of pancreatic cancer metabolism articles, to summarize the research trends and predict research hotspots. METHODS: We searched, screened and downloaded articles on pancreatic cancer metabolism through the Web of Science Core Collection (WoSCC). Using CiteSpace, VOSviewer and Bibliometrix Package to analyze publications, countries/regions, authors, institutions, journals, co-cited references, and keywords of pancreatic cancer metabolism to identify research trends and predict research hotspots. RESULTS: According to the inclusion and exclusion criteria, a total of 5,255 articles were retrieved during the period 1943-2022. The number of publications on pancreatic cancer metabolism is increasing year by year. The United States (n=1602, 30.49%), China (n=1074, 20.44%), and Italy (n=313, 5.96%) are the three countries with the largest number of publications and citations, and there is close cooperation between countries. LI J (n=55) is the most prolific author. FUDAN UNIV (n=348) is the most published institution. CANCERS (n=118), PLOS ONE (n=93), and CANCER RESEARCH (n=80) are the most popular journals in this field. Nutriment-deficient environment , cancer chemoprevention and targeting cancer stem cell are the main areas of focus. immunotherapy , ferroptosis and targeted therapy are hot keywords in recent years. Taking pancreatic cancer metabolism as an entry point to study the role of traditional Chinese medicine (TCM) mainly focuses on curcumin and resveratrol, lack of broader and deeper research on TCM. CONCLUSIONS: The number of publications on pancreatic cancer metabolism has generally increased, and scholars have generally paid more attention to this field. immunotherapy , ferroptosis and targeted therapy are the current research hotspots. The in-depth study of pancreatic cancer metabolism will provide new ideas for the treatment of pancreatic cancer

Origin of two-band chorus in the radiation belt of Earth.

Naturally occurring chorus emissions are a class of electromagnetic waves found in the space environments of the Earth and other magnetized planets. They play an essential role in accelerating high-energy electrons forming the hazardous radiation belt environment. Chorus typically occurs in two distinct frequency bands separated by a gap. The origin of this two-band structure remains a 50-year old question. Here we report, using NASA's Van Allen Probe measurements, that banded chorus waves are commonly accompanied by two separate anisotropic electron components. Using numerical simulations, we show that the initially excited single-band chorus waves alter the electron distribution immediately via Landau resonance, and suppress the electron anisotropy at medium energies. This naturally divides the electron anisotropy into a low and a high energy components which excite the upper-band and lower-band chorus waves, respectively. This mechanism may also apply to the generation of chorus waves in other magnetized planetary magnetospheres

Potential Chemoprotective Effects of Active Ingredients in Salvia miltiorrhiza on Doxorubicin-Induced Cardiotoxicity: A Systematic Review of In Vitro and In Vivo Studies

BACKGROUND: Recently, attention has been paid to the protective properties of active ingredients in METHODS: According to the PRISMA guideline, the current systematic review was conducted in the Web of Science, PubMed, Embase, and the Cochrane Library to collect all relevant RESULTS: Twenty-two eligible articles were included in this systematic review. Eleven types of active ingredients in CONCLUSION: This systematic review demonstrated that AISM have chemoprotective effects on DI