Seeing the Invisible: Understanding the Implications of Social Media Invisible Responses for Well-Being and Relational Development

Large swathes of current social media scholarship monolithically treats browsing behaviors as passive behaviors, per the passive versus active behaviors approach to social media activities. Such labeling fails to capture the numerous ways that people respond to social media sharing beyond visible clicks on the platform, such as relational distancing or switching channels to respond. Moreover, understanding what people do with information seen on social media platforms and how they respond to such information is integral to theorizing the implications of using these platforms. My dissertation tackles these challenges by first proposing the concept of invisible responses to unify the diverse approaches of responding possible to social media. Specifically, I define invisible responses as reactions to social media sharing by viewers that are invisible along any of the following dimensions: (1) to the original platform, (2) to the sharer, and (3) to the viewer’s imagined audience of third parties. The dissertation presents three empirical studies to investigate the different dimensions of invisible responses. Study 1 examines viewing time and visible clicks while browsing Facebook feeds. While viewing time—a proxy for visual attention—is largely invisible, public feedback of clicks are visible to everyone. Study 1 reveals that the combination of these two types of responses, along with the amount of social content in feeds, can predict important well-being outcome, namely self-esteem. Study 2 explores how people practice self-presentation and relational maintenance in an environment where responses are invisible to third parties. These findings suggest that, given responses that are invisible to third parties, sharers feel lower self-presentational pressure. However, what remains unchanged compared to public feedback is the emphasis on the amount of attention and effort as signaling investment in a relationship. Finally, Study 3 investigates when and why people would make their reception of a social media post invisible or not to the original sharer of the post. Once again, the findings underscore that sending signals of attention and effort is meaningful for relational maintenance. Together, the studies in this dissertation illustrate the importance of invisible responses in understanding well-being and relational outcomes of social media use, as well as opening up future avenues for research. Specifically, responding to the research agenda outlined by the communication visibility theory (Treem, Leonardi, & van den Hooff, 2020), I highlight questions around the management of visibility on social media.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162891/1/dieptl_1.pd

Business-Linkage Volatility Spillovers Between US Industries

We examine the volatility transmission across industries and its dependence on the inter-industry business linkages. Our analysis reveals significant cross-industry volatility spillovers, which are clearly associated with the strength of the trade relationship between industries. An industry that is more important to its trade partner-as measured by the shares of inputs or revenue-tends to have stronger volatility spillovers toward its partner and it is less affected by the volatility originating from its partner. Importantly, the strength of the business relationship appears highly relevant for shock spillovers in bad market conditions and is also confirmed at the portfolio level

Một cơ chế quản lý hàng đợi tích cực cải tiến VBLUE trên môi trường truyển video

The rapid development of video streaming applications over the internet poses a growing challenge. The requirements for bandwidth capacity and latency of transmission package often change constantly. One of the queue management mechanisms commonly used to increase the performance and prevent the degradation of video transmission quality is the active queue management mechanism (AQM). However, though the internet is a good effort network it does not have distinctions among transmission packets on the network leading to a significant percentage of video data packet discarded by the network router upon the occurrence of lacking bandwidth on the traffic lines due to congestion. The impact of the lost video package degrading the quality of watching at the receiver may vary from negligible to unacceptable levels. This paper proposes an innovative solution using selected VBLUE to discard the package which is built in the BLUE active queue management mechanism. The simulation results on NS-2 are given to show the efficiency of VBLUE for increasing the significant quality of video streaming.Sự phát triển nhanh chóng các ứng dụng truyền video trên Internet đặt ra những thách thức ngày càng lớn. Các yêu cầu về khả năng băng thông và độ trễ truyền dẫn gói tin thường biến đổi liên tục. Một trong các cơ chế quản lý hàng đợi thường được sử dụng để tăng hiệu năng mạng và ngăn cản sự suy giảm chất lượng truyền video là cơ chế quản lý hàng đợi tích cực (AQM). Tuy nhiên, do mạng Internet là mạng mặc dù được xây dựng với nỗ lực tối đa nhưng chưa thể đảm bảo về QoS (best-effort network) và không có sự phân biệt giữa các gói tin truyền trên mạng dẫn đến tỷ lệ đáng kể các gói dữ liệu video bị loại bỏ bởi các bộ định tuyến mạng khi xảy ra tình trạng thiếu băng thông trên các đường truyền do bị tắc nghẽn. Ảnh hưởng của việc mất gói tin video làm suy giảm chất lượng xem ở phía máy nhận có thể thay đổi từ không đáng kể đến mức không thể chấp nhận được. Bài báo này đề xuất một giải pháp cải tiến VBLUE sử dụng lựa chọn loại bỏ gói tin được tích hợp ngay trong cơ chế hàng đợi tích cực BLUE. Các kết quả mô phỏng trên NS-2 đã cho thấy hiệu quả của VBLUE làm tăng chất lượng phát luồng video một cách đáng kể.  

Circulating Biomarkers for Early Diagnosis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, which is also often fatal. An early and accurate diagnosis is a decisive step towards the survival of the patients. Molecular biology improved significantly the prognosis of liver cancers through learned use of tumor markers like proteantigens, cytokines, enzymes, isoenzymes, circulating RNAs, gene mutations and methylations. Nevertheless, much improvement is still achievable and needed in this area, which is crucial in order to make an early diagnosis and monitor the progression of the disease. We present in this review what we believe to be the most relevant data regarding tissue and serum biomarkers related to HCC

Geochemical characterization of groundwater and saltwater intrusion processes along the Luy River, Binh Thuan, Vietnam

With an average annual rainfall of 800-1150 mm/year, the Binh Thuan province is one of the driest places in Vietnam. The quantity and quality of groundwater play a significant role in the agriculture, aquaculture development and daily life of the local communities. In 2012, the national centre for water resources (Nawapi, 2012) delineated the seawater intrusion extent in Binh Thuan based on the total dissolved solids (TDS) content of water samples taken from shallow boreholes. The threshold of 3g/L and 1.5g/L were exceeded in the estuaries of the Luy, Long Song and Ca Ty rivers. In recent years, the prolonged droughts combined with the sea level rise and the over-extraction of groundwater during the dry season increased dramatically the seawater intrusion process especially in the estuaries of the province. The geochemistry of groundwater in the Luy River catchment was studied to investigate the contamination of the aquifers and identify the processes taking place. From 1991 to 2015, 98 water samples had been taken from the wells in the area in both dry and rainy seasons. 71% of the water samples were fresh while 21% and 5% were lightly saline and moderately saline respectively. In summer 2020, 110 new water samples from both shallow and deep wells were collected in the Luy river catchment in wells from 3m to 40m. The TDS values are ranging from 105 to 23080 mg/L and can be classified into 4 groups: freshwater (48%), slightly saline (40%), moderately saline (8%) and very saline (4%). The samples show that the seawater intrusion expands not only horizontally at shallow depth along the river but also deeper down the aquifer in most of the study area, what is also confirmed by geophysical data. Freshwater samples were mostly collected at a depth lower than 10m. The chemical composition of water samples was analyzed showing evidence of seawater intrusion, but also the occurrence of freshening processes within the study area. Together with the presence of saltwater at larger depths, this points towards a situation more complex than previously thought. Saltwater intrusions are likely not only related to interaction with the river estuary, but also to the presence of fossil saltwater in the aquifer, and to groundwater pumping and irrigation practices

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

THE FACTORS EFFECT TO THE FINANCIAL EFFICIENCY OF FDI (FOREIGN DIRECT INVESTMENT) ENTERPRISES LOCATED IN HO CHI MINH CITY-VIET NAM

ABSTRAC

THE FACTORS EFFECT TO THE FINANCIAL EFFICIENCY OF FDI (FOREIGN DIRECT INVESTMENT) ENTERPRISES LOCATED IN HO CHI MINH CITY-VIET NAM

ABSTRAC

Chemical profile and antibacterial activity of acetone extract of Homalomena cochinchinensis Engl. (Araceae)

Homalomena cochinchinensis Engl. is a rare species which is found in Southern China, Cambodia, Laos and Vietnam and its chemical constituents and bioactivity have not been determined yet. In this study, we identified 32 and 38 compounds in acetone extracts of H. cochinchinensis aerial part and rhizome, respectively via gas chromatography mass spectrometry (GC/MS). The main constituents of acetone extract of the aerial part were 3-((4Z,7Z)-Heptadeca-4,7-dien-1-yl)phenol (18.73%); cis-9,cis-12-Octadecadienoic acid (12.04%); linolenic acid (11.08%); n-Hexadecanoic acid (10.13%); (Z)-3-(Heptadec-10-en-1-yl)phenol (7.09%); ?-Sitosterol (5.58%) and linalool (5.56%). On the other hand, acetone extract of rhizome contained linalool (28.42%); 1,2,3-Propanetriol, 1-acetate (10.13%); 3-((4Z,7Z)-Heptadeca-4,7-dien-1-yl)phenol (5.28%); 3-Buten-2-one, 3-methyl-4-(1,3,3-trimethyl-7-oxabicyclo[4.1.0]heptan-1-yl)- (5.28%) and 4-(2,6,6-Trimethyl-cyclohex-1-enyl)-butyric acid (4.54%). Furthermore, this study has also proved the antibacterial activity of acetone extracts from the aerial part and the rhizome of this species for the first time using disk diffusion method. The results showed that the extract of the aerial part could inhibit the growth of 5 out of a total 6 bacterial strains, including Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa, Salmonella enteritidis and Staphylococcus aureus; while the susceptible strains to the rhizome extract were 5 strains, such as B. cereus, E. coli, P. aeruginosa, Salmonella typhimurium and S. aureus. The findings suggest the further application of this species in pharmacology and medicine

An Exploration into the Benefits of the CLIP model for Lifelog Retrieval

In this paper, we attempt to fine-tune the CLIP (Contrastive Language-Image Pre-Training) model on the Lifelog Question Answering dataset (LLQA) to investigate retrieval performance of the fine-tuned model over the zero-shot baseline model. We train the model adopting a weight space ensembling approach using a modified loss function to take into account the differences in our dataset (LLQA) when compared with the dataset the CLIP model was originally pretrained on. We further evaluate our fine-tuned model using visual as well as multimodal queries on multiple retrieval tasks, demonstrating improved performance over the zero-shot baseline model