6 research outputs found
A Secure Off-Line MICO Payment Approach Using Multiple Physical Unclonable Functions
FRoDO, a protected off-line micro-payment approach utilizing various physical unclonable capacities. FRoDO highlights an identity component to verify the client, and a coin component where coins are not locally stored, but rather are processed on-the-fly when required. The communication protocol utilized for the payment exchange does not directly read client coins. Rather, the seller just speaks with the personality component keeping in mind the end goal to recognize the client. This rearrangements eases the communication trouble with the coin component that influenced our past approach. The fundamental advantage is a less complex, speedier, and more secure cooperation between the included performing actors/entities. Among different properties, this two-stage protocol permits the bank or the coin component guarantor to outline computerized coins to be perused just by a specific character component, i.e. by a particular client. Besides, the character component used to enhance the security of the clients can likewise be utilized to obstruct malicious clients. To the best of our insight, this is the principal arrangement that can give secure completely off-line payments while being flexible to all as of now known PoS breaches
Accountability in the Design Phase of a Data Transfer Infrastructure
We propose a novel response for cross-site cold-start thing recommendation, which expects to we demonstrate a nonspecific data parentage structure LIME for data stream over various components that take two trademark, principle parts (i.e., proprietor and purchaser). We portray the right security guarantees required by such a data heredity framework toward recognizing confirmation of a subject substance, and perceive the streamlining non-disavowal and validity suppositions. We by then make and separate a novel mindful data trade tradition between two components inside a poisonous circumstance by developing unaware trade, enthusiastic watermarking, and check primitives. Finally, we play out a trial appraisal to show the judgment skills of our tradition and apply our framework to the key data spillage circumstances of data outsourcing and relational associations
A Study to Learn Robust and Discriminative Representation to Tackle Cyber bullying Detection
We build up another content portrayal display in view of a variation of SDA: marginalized stacked denoising autoencoders (mSDA), which receives straight rather than nonlinear projection to quicken preparing and minimizes endless clamor dispersion so as to take in more strong portrayals. We use semantic data to grow mSDA and create Semantic-improved Marginalized Stacked Denoising Autoencoders (smSDA). The semantic data comprises of harassing words. A programmed extraction of tormenting words in view of word embeddings is proposed so that the included human work can be diminished. Amid preparing of smSDA, we endeavor to reproduce bullying highlights from other typical words by finding the idle structure, i.e. connection, amongst tormenting and typical words. The instinct behind this thought is that some harassing messages don't contain bullying words
A Matrix Framework Factorization on a Sentiment Based Rating Prediction Method tackles Cyber bullying Detection
It displays a great chance to share our perspectives for different items we buy. In any case, we confront the data over-overloading issue. Instructions to mine profitable data from audits to comprehend a client's inclinations and make an exact proposal is critical. Customary recommender systems (RS) think of some as variables, for example, client's buy records, item classification, and geographic area. In this work, we propose a supposition based rating prediction technique (RPS) to enhance expectation exactness in recommender systems. In this paper, we extricate item highlights from literary audits utilizing LDA. We for the most part need to get the item highlights including some named elements and some item/thing/benefit characteristics. LDA is a Bayesian model, which is used to show the relationship of audits, points and words.
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries