answer:A straightforward way to return a deep copy of an object without knowing whether it's cloneable is to serialize the object into a byte stream then deserialize it back into a new object. This process completely detaches the new object and the original one. Here is an example alone with necessary exception handling:```javaimport java.io.*;public class DeepCopier { public static Object deepCopy(Object original) { Object deepCopy = null; try { ByteArrayOutputStream byteOutputStream = new ByteArrayOutputStream(); ObjectOutputStream objOutputStream = new ObjectOutputStream(byteOutputStream); objOutputStream.writeObject(original); objOutputStream.flush(); ObjectInputStream objInputStream = new ObjectInputStream( new ByteArrayInputStream(byteOutputStream.toByteArray())); deepCopy = objInputStream.readObject(); } catch (IOException | ClassNotFoundException e) { e.printStackTrace(); } return deepCopy; }}```Note that this method requires the class of original, and any classes that it refers to, must implement the Serializable interface.If we apply this method to the example of the ArrayList, where you want to return a copy of the object:```javapublic class ObjectWrapper { private ArrayList<Object> objectsList; // .... other methods .... public Object getObject(int index) { Object obj = objectsList.get(index); return DeepCopier.deepCopy(obj); }}```This way, the caller of `getObject()` will receive a deep copy of the original object, so any further modifications to the original object in the ArrayList will not affect the copy.

answer:Let's break this problem down:1. The congruence relation x^y equiv y^x mod n looks similar to the original equation x^y = y^x. However, mod n restricts the solutions to a finite set that depends on n. 2. Function f(n) is the count of solutions in a certain range. This implies, it is feasible to compute f(n) in a brute-force manner for small n, but for larger n we need a more efficient approach. 3. Function S(M, N) is the sum of f(p) over all primes p in the interval (M,N). This suggests that we need some way to efficiently generate all primes in a range, and then compute f(p) for each. 4. The primary task is to find S(10^{16}, 10^{16}+10^6) mod{993353399}. Given the magnitude of these numbers, it's clear that a brute-force solution isn't feasible. We'll need to look for some kind of pattern or mathematical formula.Insight: x^y equiv y^x mod p where p is a prime. Now, from Fermat's little theorem, we have x^{p-1} equiv 1 mod p for all x not divisible by p. Re-writing the given congruence, we get x^{y}*(y^{x} mod p) = 0. Since y and x can't be p(divisible by p) because 0 < x, y leq p^2 - p, we must have y^{x} mod p = 0. The equation will then only hold where y is divisible by p. Consider p^2-p = p*(p-1). All solutions for y, provided that x^p equiv 0 mod p, will be of the form p, 2p, ..., (p-1)p. Hence, there are p-1 solutions for each x which will give a total of (p-1)^2 solutions.The python code with this optimization will be:```pythondef S(M, N): primes = generate_primes(M, N) # function to generate primes in range s = sum([(p-1)2 for p in primes]) return s % 993353399print(S(1016, 1016 + 106))```Note that we've reduced the problem essentially to needing a function `generate_primes(M, N)` that generates all primes in the range (M, N). It is a common task and can be accomplished by different algorithms like Sieve of Eratosthenes, Segmented Sieve, etc. which are not shown here.Remember the actual calculation of S(10^{16}, 10^{16}+10^6) can be very time and resource intensive even with the efficient approach. It's a large scale computation problem.

answer:Based on the analysis and comparison on the existing works in Section V, we outline several open issues on secure data analytics in edge computing as follows. First of all, how to balance security and efficiency is still an open problem. From Table IV , we can observe that most works need user devices or edge nodes to perform some complex operations, which incurs high computation cost. In fact, some IoT applications in edge computing have a high demand on real-time response. The complex computation obviously impacts efficiency, especially for resource-constrained user devices. Therefore, making a tradeoff between security and efficiency becomes necessary in many practical situations. Second, the trustworthiness of networked devices in highly distributed network still need further researches. Though there are a few studies BIB003 , BIB001 , BIB004 on trustworthiness of networked devices, they still have some disadvantages. For example, the works in BIB003 and BIB004 consider only single application scenario and the study in BIB001 does not support mobility and scalability. However, device trustworthiness is an important demand to ensure the QoS of edge nodes and prevent a device from malfunction. Thus, an effective trust model for edge computing should be proposed. Third, UP is ignored in all reviewed works. However, it is very necessary to protect UP in edge computing. Since edge computing is designed specifically for IoT applications, the collected data contain user behavior information and living habit information. An eavesdropper or an edge node could easily obtain usage patterns even if data are encrypted before uploading to an edge server. For example, an eavesdropper can predict when a user is at home through the changes of the readings of a smart meter. Fourth, the literature still lacks an effective solution to verify the correctness of data computation when utilizing edge nodes to do data analytics. The correctness of calculation remains high importance in outsourced data analytics BIB005 , BIB002 . In edge computing, neither cloud servers nor edge nodes can be fully trusted, which makes it hard to ensure the correctness of data computation, processing and analytics. If there is no security solution to guarantee the above correctness, end users will be reluctant to use the services provided by edge computing. Fifth, mobility and scalability of mobile devices cannot be well supported in the current literature. Most existing works neglect the mobility and scalability of user devices when designing security schemes for edge computing. However, user devices and edge nodes might frequently migrate from one place to another. At the same time, user devices could quickly join or leave an edge network. Thus, a security scheme should support mobility and scalability in the context of edge computing, however, this is still an open issue.

answer:{"Chemical": ["androgens", "5alpha-dihydrotestosterone", "DHT", "androgen", "Androgens", "testosterone"], "Protein": ["nuclear receptors", "androgen receptor", "nuclear receptor"]}

answer:["A"]

answer:Here are the steps to implement Redshift Spectrum tables with Terraform and SerDe:1. Install Terraform: The first thing needed to start with Terraform is to install its latest version. Terraform is available for download on macOS, Linux and Windows.2. AWS Configuration: You will need to set up your AWS credentials. You can set up your AWS access keys directly into your .tf files but this method is not recommended for security reasons. It's better to configure your credentials using the AWS CLI `aws configure`.3. Create a Terraform Project: Start up by creating a directory where your Terraform files will live.4. Writing and Configuring the Terraform files: So suppose we are creating an Amazon Redshift cluster and defining a Redshift Spectrum table. We'll need to use `aws_redshift_cluster` resource and `aws_glue_catalog_table` resource in our Terraform script. It might look something similar to:```resource "aws_redshift_cluster" "default" { // Your Redshift configuration here.}resource "aws_glue_catalog_table" "example" { name = "example" database_name = aws_glue_catalog_database.example.name table_type = "EXTERNAL_TABLE" // This is where you mention your SerDe. parameters = { EXTERNAL = "TRUE" "serialization.format" = "1" } storage_descriptor { // Your Storage description here like Location, Input format etc. }}```Remember to replace appropriate values and properties in the script.5. Initialize Terraform: In your terminal, cd into the directory with your .tf files and run the `terraform init` command. This will initialize Terraform in your directory.6. Plan and Apply: See what Terraform plans on doing with the `terraform plan` command and then apply those changes with `terraform apply`7. Verify: Login to the AWS Console to verify if changes have been made.This is a basic implementation of Terraform, AWS Redshift and Spectrum. Remember to adapt according to your use case.