research

Realizing the Potential of Machine Learning with Python Libraries

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In the realm of data science, machine learning stands out as a powerful approach to problem-solving by harnessing the potential of data. Unlike traditional programming, where solutions are explicitly defined, machine learning involves enabling computers to learn and find solutions autonomously. This article will focus on the pivotal role of machine learning libraries in Python, highlighting their significance in creating and training machine learning models for a variety of applications.

Python offers a plethora of libraries dedicated to machine learning, each with its own unique strengths and capabilities. These libraries have been instrumental in shaping my journey as a researcher, enabling me to unlock valuable insights and make data-driven decisions. Alongside a team of skilled researchers, I have had the privilege of utilizing various libraries, with Scikit-learn playing a particularly vital role in our work.

Scikit-learn, built on top of the powerful NumPy and SciPy libraries, has been an invaluable asset in our machine learning endeavors. Its vast collection of classes and functions provides a solid foundation for implementing traditional machine learning algorithms. From classification and regression to clustering and dimensionality reduction, Scikit-learn has been our go-to library for a wide range of tasks.

However, the power of machine learning extends beyond our research endeavors. As we explored earlier in our blog series, machine learning has proven to be an indispensable tool for threat hunting. By leveraging the capabilities of machine learning libraries, organizations can effectively detect and combat cyber threats, enhancing their security posture and safeguarding sensitive data.

Now, let us delve into some of the popular machine learning libraries that have significantly improved the field:

  1. TensorFlow: Renowned as a leading framework in deep learning, TensorFlow enables the resolution of intricate problems by defining data transformation layers and fine-tuning them iteratively. Its extensive ecosystem and diverse set of tools make it a preferred choice for constructing and training sophisticated deep learning models.
  2. PyTorch: Positioned as a robust and production-ready machine learning library that has garnered significant recognition, PyTorch excels in addressing complex deep learning challenges by harnessing the computational power of GPUs. Its dynamic computational graph and intuitive interface make PyTorch a preferred choice for flexible and efficient model development.
  3. Keras: Renowned for its user-friendly interface and high-level abstractions, Keras simplifies the development of neural networks. Its seamless integration with TensorFlow enables rapid prototyping and deployment of deep learning models.

These machine learning libraries—Scikit-learn, TensorFlow, Keras, and PyTorch—play an indispensable role in unlocking the predictive potential of data and driving innovation across diverse domains.

In summary, Python’s rich ecosystem of machine learning libraries are powerful tools for building, training, and deploying machine learning models. Through my own usage and exploration, I have found these libraries to be incredibly helpful, with Scikit-learn being particularly influential in my work. Furthermore, the impact of machine learning extends to critical domains such as threat hunting and cyber security, empowering organizations to proactively address emerging threats and safeguard their valuable assets.

Unveiling the Lack of Transparency in AI Research

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A recent systematic review by Burak Kocak MD et al. has revealed a lack of transparency in AI research. The data, presented in Academic Radiology, showed that only 18% of the 194 selected radiology and nuclear medicine studies included in the analysis had raw data available, with access to private data in only one paper. Additionally, just one-tenth of the selected papers shared the pre-modeling, modeling, or post-modeling files.

The authors of the study attributed this lack of availability mainly to the regulatory hurdles that need to be overcome in order to address privacy concerns. The authors suggested that manuscript authors, peer-reviewers, and journal editors could help make AI studies more reproducible in the future by being conscious of transparency and data/code availability when publishing research results.

The findings of the study highlight the importance of transparency in AI research. Without access to data and code, it is difficult to validate and replicate results, leading to a lack of trust in the results. This is especially important for medical AI research, as the safety and efficacy of treatments and diagnostics depend on accurate and reliable results. What further steps can be taken to increase transparency while still protecting privacy?

8 Rules for Good Research Practice

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As a researcher, it is important to understand good research practices and to make sure to adhere to them. This article will delve into each of the eight rules proposed by the Swedish Research Council (Vetenskapsrådet, 2017) for good research practice and provide examples of how to apply them in your own research.

  • 1. To tell the truth about one’s research. This means being honest and open about the methods and results of your research. It also means not making false claims or manipulating data to fit a desired outcome. To ensure that your research is truthful, make sure to accurately record your data and to clearly explain any methods or results that are not obvious. It is also important to keep an open mind when conducting research; be willing to question your own assumptions and consider alternative explanations.
  • 2. To consciously review and report the basic premises of one’s studies. When conducting research, it is important to be aware of the assumptions and premises of your work. Make sure to clearly explain why you are conducting the research, what results in you expect, and how the research will be used. This will help to ensure that the research is conducted in a sound and ethical manner.
  • 3. To openly account for one’s methods and results. When conducting research, it is important to clearly explain the methods and results that were used in the study. This includes explaining the rationale behind the methods, the results that were obtained, and any limitations or weaknesses that were encountered. Doing so will help to make sure that the research is conducted in an ethical manner and that the results are accurate and meaningful.
  • 4. To openly account for one’s commercial interests and other associations. When conducting research, it is important to be aware of any financial or other interests that may affect the results of the study. Make sure to disclose any potential conflicts of interest, such as funding sources, collaborations, or affiliations. This will help to ensure that the research is conducted in an ethical manner and that the results are unbiased.
  • 5. To not make unauthorized use of the research results of others. When conducting research, it is important to respect the intellectual property of others. Make sure to properly cite any sources that you use and to get permission before using the research results of others. Doing so will help to ensure that you are not infringing on the rights of others and will help to protect your own work from potential misuse.
  • 6. To keep one’s research organized, for example, through documentation and filing. When conducting research, it is important to keep track of the data and results that you obtain. Make sure to keep accurate records and to store data in a secure manner. Doing so will help to ensure that the research is conducted in an orderly and ethical manner and will help to protect the integrity of the research.
  • 7. Striving to conduct one’s research without doing harm to people, animals, or the environment. When conducting research, it is important to be aware of the potential consequences of the study. Make sure to consider any ethical implications of the research and to take necessary steps to minimize any potential risks or harms. This will help to ensure that the research is conducted ethically and with respect for the rights of participants, animals, and the environment.
  • 8. To be fair in one’s judgement of others’ research. When conducting research, it is important to be aware of the potential biases that may influence one’s judgement. Make sure to consider the context of the research and to keep an open mind when evaluating the work of others. Doing so will help to ensure that your own research is conducted in an ethical manner and that the results are reliable.

In conclusion, it is important for researchers to be aware of and adhere to the ethical guidelines and principles of good research practice. By understanding and following these eight rules proposed by the Swedish Research Council, researchers can ensure that their work is conducted in an ethical and responsible manner.

The Importance of Combining Research and Teaching

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As the world progresses, so too does the need for innovative research to support it. In many ways, research and teaching go hand-in-hand, with each feeding off the other to produce a well-rounded system of knowledge. In the field of cybersecurity, for example, teaching is essential to ensure that a new generation of workers is equipped with the skills they need to protect our online world. But research is also critical to staying ahead of the curve and developing new ways to combat the ever-evolving threats that target our digital lives.

The benefits of combining research and teaching are numerous. By keeping up with the latest advances in their field, teachers can ensure that their students are receiving the most up-to-date and relevant information. This helps to prepare students for the real world, where they will be expected to apply their knowledge to solve problems. Meanwhile, researchers can use their findings to inform their teaching, ensuring that the latest discoveries are passed on to the next generation.

But it is not just about staying up-to-date; research can also help to improve the quality of teaching. By constantly testing and refining their methods, researchers can develop more effective ways of imparting knowledge. This benefits not only the students who receive this improved teaching but also society as a whole, as a better-educated workforce is better equipped to meet the challenges of the 21st century.

It is clear, then, that research and teaching are two sides of the same coin. By working together, they can create a virtuous circle that benefits everyone involved.

I Had a Great Time at the WEBIST 2022 Conference

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I had a wonderful time at the WEBIST 2022 conference in Malta, where I presented my research article titled “A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups“.

My presentation was on Thursday, October 27th, and it went really well. My audience was engaged throughout, and they asked some great questions at the end. I think they appreciated hearing about my approach to anomaly detection in interactive machine learning setups that include regular people interacting with IoT sensors for online learning purposes—which is an area that has not received much attention so far. With the help of supervised machine learning techniques, data poisoning attacks and potentially zero-day attacks can be detected with high-accuracy without requiring any hard-coded rules.

I was also honored to be selected as session chair for the Internet Technology track. The conference ran smoothly thanks to everyone’s efforts, and I am very thankful to have been chosen as chair.

I would like to thank all the organizers for a very well-organized event and the other participants for making it such a productive one.

I am looking forward to attending future WEBIST conferences and continuing to build my network.

The Benefits of Industry Experience for Academics

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Some people may say that having industry experience is essential to being a successful academic, while others may argue that it is not necessary. It is important to consider both sides of the argument before making a decision.

Those who argue that industry experience is necessary may say that it is essential in order to understand the real-world applications of your research. They may also argue that industry experience can help you build important networks and connections. Those who argue that industry experience is not necessary may say that academic research is theoretical and that real-world experience is not relevant. They may also argue that you can gain all the skills and experience you need by working in academia.

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It is important to weigh both sides of the argument before deciding whether or not industry experience is necessary for you. If you are still undecided, you may want to speak to academics who have both industry experience and academic experience to get their opinion. Nonetheless, I believe that industry experience can be beneficial for academics. Here are five ways that industry experience can help you:

1. Industry experience can help you get a job. If you are looking for a job in academia, industry experience can make you a more attractive candidate. Employers will see that you have real-world experience and that you are familiar with the industry. 

2. Industry experience can help you with your research. If you are doing research for your Ph.D., industry experience can be beneficial. You will probably be able to apply your research to real-world scenarios, and you will have a better understanding of the industry. 

3. Industry experience can help you network. Networking is important for both your academic career and your Ph.D. studies. Industry experience can help you meet people in your field and make connections. 

4. Industry experience can help you get funding. If you are applying for grants or funding for your research, industry experience can be helpful. Funding organizations will see that you have experience in the industry and that your research is relevant to the industry. 

5. Industry experience can help you teach. If you are teaching at the university level, industry experience can be beneficial. Students will see that you have real-world experience and that you are familiar with the industry.

You are welcome to contact me if you are interested in learning more about my experience with this, or simply if you want to collaborate with me.

How To Achieve Flow When Writing A Research Paper

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When working on your Ph.D., it is imperative to maintain a state of flow. Maintaining a state of flow will give you more motivation towards achieving your goals and finishing work in a timely manner. In addition, maintaining a state of flow will help keep you from getting distracted by less important tasks that are not related to your work, such as checking Instagram, TikTok, Twitter, and other social media platforms.

As Mihaly Csikszentmihalyi asserts in his book “Flow: The Psychology of Optimal Experience”, flow is “the state in which people are so involved in an activity that nothing else seems to matter; the experience itself is so enjoyable that people will do it even at great cost, for the sheer sake of doing it”. Flow is a state in which you are so focused on your work that all other distractions fade away. It is a state in which you are completely immersed in what you are doing, and it is very difficult to pull you out of it. Many Ph.D. students struggle with maintaining a state of flow in their research work. They want to work hard and do well, but they just cannot seem to find the motivation or energy needed to complete their projects. Here are four tips that might help you achieve flow:

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1)  If you want to be able to write effectively, then you need to set aside time each day for writing. This can be done by creating a schedule and sticking to it. The schedule needs to include the amount of time that you will be spending on your writing tasks each day, as well as any breaks that you plan on taking during the day.

2) Before sitting down, make sure you have everything ready — your notes/laptop/pencils/paper/etc. — so that when it comes time to work on something important, there are no delays caused by having to look for something else first, rather than just getting started right away without any more delays than necessary.

3) Disable any notifications from social media platforms including televisions and phones so that they do not distract you while working on your project. You can always check these platforms or media after completing your tasks for the day, but if they are distracting, then they should be turned off for better focus during working hours.

4) Take frequent breaks, but keep them short and to the point. Consider also using the Pomodoro Technique. The Pomodoro Technique is a time management technique where you work using 25-minute work sprints and then enjoy a 5-minute break.

It is just as important to maintain a state of flow as it is to establish one at the start of your work. The two things go hand in hand. However, maintaining flow can be more difficult at times because some things that interrupt flow are not always avoidable. In order to stay on top of your Ph.D. while maintaining a state of flow, you need to be organized and efficient. By setting aside enough time each day for writing and making sure that you have everything ready before sitting down to work, you will be able to focus more fully on the task at hand, which should contribute towards maintaining a state of flow.

Popular smart home brands may be allowing the police to conduct warrantless home surveillance

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The security cameras in our smart homes from well-known smart home brands like Amazon and Google might not just be watching over our pets. According to an article in The Verge, they can also aid law enforcement in their investigations of crimes, but only if we do not mind the police viewing our footage without a warrant.

That implies that the police can access our private information without first presenting proof that an emergency situation exists. Police will probably only make use of this access for lawful objectives, such as preventing crime or attempting to locate a missing person in need of assistance. However, it does raise some issues regarding what may transpire when this technology becomes even more widely used and available.

What if, for instance, this access is utilized to locate and detain activists or protestors who have not breached any laws? Citizens may only exercise caution when shopping, be aware that their smart device may record personal information, and, if possible, enable end-to-end encryption.

If you have any questions about how to secure your smart home, do not hesitate to contact me.

Do You Need to Wait for Perfect Results Before Publishing?

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You are lucky if you are already thinking about writing your first scientific publication based on your Ph.D. work. Writing a publication is one of the most important skills that any researcher must acquire during their Ph.D. period. However, students often ask the question, of whether they should wait for “perfect” results before publishing their first paper.

My advice is to write when your work is mature enough that you can cut it into bite-sized pieces that interest others, and then go ahead and start writing. At the end of the day, writing is a cyclical process, and you can only get better at it by writing. Apart from the personal satisfaction of completing an article and seeing it accepted in a peer-reviewed journal/conference, it gets you excited about the project (it gives you motivation). It also makes it much easier for you to progressively make progress by writing in steps and establishing milestones. In doing so, when you get to the final rewriting stage, it will not feel like a mountain to climb. It should be something that builds naturally upon what has already been done.

So, I personally think that the quicker you start to publish, the quicker you will build up a track record of publications. You can use this track record to get funding or a job at the end of your Ph.D., which is key for your career.

The CNIL’s Privacy Research Day

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The first CNIL’s International Conference on Research in Privacy took place in Paris yesterday, June 28, and was broadcast online for free. In addition to providing a great opportunity to consider the influence of research on regulation and vice versa, this conference facilitated the building of bridges between regulators and researchers.

During the day, experts from different fields presented their work and discussed its impact on regulation and vice-versa. I attended it online — there were many interesting topics covered by the different panelists. The topics ranged from the economics of privacy, smartphones and apps, AI and explanation, and more. Surely, one of the panels that I liked was that on AI and explanation. 

Machine learning algorithms are becoming more prevalent, so it is important to examine other factors in addition to optimal performance when evaluating them. Among these factors, privacy, ethics, and explainability should be given more attention. Many of the interesting pieces I see here are related to what I and my colleagues are working on right now and what I have planned for my upcoming projects.

You are welcome to contact me if you are curious about what I am working on and would want to collaborate.