Organisational preparedness for hosted virtual desktops in the context of digital forensics

Virtualization in computing has progressed to an extent where desktops can be virtualized and accessed from anywhere. The server hosted model has already surpassed 1% market share of the worldwide professional PC market, with estimates indicating that this is a rapidly growing area. This paper investigates the adequacy of current digital forensic procedures on hosted virtual desktops (HVDs) as there does not appear to be specific methods of locating and extracting evidences from this infrastructure. A hosted virtual desktop deployed in private clouds was simulated to reflect two different computer crime scenarios. It was found that current digital forensic procedures may not be adequate for locating and extracting evidence, since the infrastructure introduces complications such as persistent/non-persisted disk modes and segregating data in a multi-tenant environment

Unclonable Non-Interactive Zero-Knowledge

A non-interactive ZK (NIZK) proof enables verification of NP statements without revealing secrets about them. However, an adversary that obtains a NIZK proof may be able to clone this proof and distribute arbitrarily many copies of it to various entities: this is inevitable for any proof that takes the form of a classical string. In this paper, we ask whether it is possible to rely on quantum information in order to build NIZK proof systems that are impossible to clone. We define and construct unclonable non-interactive zero-knowledge proofs (of knowledge) for NP. Besides satisfying the zero-knowledge and proof of knowledge properties, these proofs additionally satisfy unclonability. Very roughly, this ensures that no adversary can split an honestly generated proof of membership of an instance $x$ in an NP language $\mathcal{L}$ and distribute copies to multiple entities that all obtain accepting proofs of membership of $x$ in $\mathcal{L}$. Our result has applications to unclonable signatures of knowledge, which we define and construct in this work; these non-interactively prevent replay attacks

Locally Covert Learning

The goal of a covert learning algorithm is to learn a function f by querying it, while ensuring that an adversary, who sees all queries and their responses, is unable to (efficiently) learn any more about f than they could learn from random input-output pairs. We focus on a relaxation that we call local covertness, in which queries are distributed across k servers and we only limit what is learnable by k - 1 colluding servers. For any constant k, we give a locally covert algorithm for efficiently learning any Fourier-sparse function (technically, our notion of learning is improper, agnostic, and with respect to the uniform distribution). Our result holds unconditionally and for computationally unbounded adversaries. Prior to our work, such an algorithm was known only for the special case of O(log n)-juntas, and only with k = 2 servers [Yuval Ishai et al., 2019]. Our main technical observation is that the original Goldreich-Levin algorithm only utilizes i.i.d. pairs of correlated queries, where each half of every pair is uniformly random. We give a simple generalization of this algorithm in which pairs are replaced by k-tuples in which any k - 1 components are jointly uniform. The cost of this generalization is that the number of queries needed grows exponentially with k

Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model

Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written,  polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove & fasttext) or context-aware (like  XLNet) to effectively represent the features needed for processing.  To improve the performace towards emotion or  sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced  dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as  MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well

New species of the genus Lytocestus (Caryophyllidea lytocestidae) from catfish in Aurangabad dist (M.S.), India

Two new caryophyllidean species of the genus Lytocestus from catfish Clarias batrachus (L.) from Aurangabad District is described. The differential characters of Lytocestus khami Sp. Nov. has elongated body bluntly tapering at both side, differentiated head, large saccular uterus

Blockchain based Real Estate using Smart Contracts

In today’s world global real estate investments have taken over more than twice the size of the stock market. Even after the dominance of the real estate market there are still a less number of investors due to liquidity and global access to sensitive or critical information. Due to various flaws in security in the current system, the tenants and owners are barely satisfied. The main focus of this paper is to incorporate the use of blockchain in the current real estate market and represent the facilities and advantages it can give to the real estate market. Blockchain based real estate is better because each block contains a cryptic hash of the previous record, a timestamp and transaction data which makes it difficult to forge documents and sensitive information of the investor. Another advantage of blockchain is that it is resistant to any type of data modification.  Blockchain technology can sort out the security issues and forgery incidents that are faced by the real estate market. Also blockchain provides much meaningful assets and insights to the real estate market at a reasonable and stable-priced market. The proposed solution for the selected problem statement is Tokenizing real estate assets refers to a process in which a property owner can offer digital tokens that represent a share of their property. Using a blockchain to track these investments, with each transaction being time-stamped and immutable, makes it possible to limit the risk of fraud

AN OPEN RANDOMIZED STUDY OF PATOLA KATUROHINYADI KASHAYAM IN ALCOHOLIC LIVER DISEASE

Alcoholic liver disease (ALD) is a leading cause of morbidity and mortality in India. Chronic consumption of alcohol results in variations in alcohol metabolism, oxidative stress, antigenic adducts formation and acetaldehyde toxicity. These factors cause inflammation, fatty changes, fibrosis of liver cells and raising the transaminases in the blood. There is no specific treatment for ALD. Patola Katurohinyadi Kashayam, a classical Ayurvedic formulation has been reported by many practitioners to be effective in treatment of liver disorders. This study focuses on the effect of the Patola Katurohinyadi Kashayam in ALD for restoration of normal liver function by investigating 10 subjective and 5 objective parameters. As Patola Katurohinyadi Kashayam is Raktaprasadak, Yakritgami, Deepan, Jwaraghna, Kamalanashak and Pandunashak it was used as Trial Drug. Clinical Trials were conducted at Anandvan De-Addiction Centre, Pune. By random allotment method 20 well-diagnosed patients of ALD were included in both Control and Trial group each. The diagnosis of ALD was made by documentation of alcohol excess and evidence of liver disease. Trial group was administered the Trial drug in a dose of 15ml with luke warm water after meal for the duration of 28 days. Control group was not given any drugs but observed for 28 days for all parameters. The statistical analysis revealed that Trial drug is effective in ALD and significantly reduces Panduta, Agnimandya, Hrullas, Daha and Daurbalya. Besides it significantly lowers the SGOT and SGPT levels too