Reviewerly/exHarmony huggingface dataset:
You Get What You Chat: Using Conversations to Personalize Search-based Recommendations

Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages in certain domains.
Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy for data driven modeling of \textit{single-input single-output} (SISO) nonlinear dynamical systems based on \textit{Genetic Programming} (GP) and \textit{Tree Adjoining Grammars} (TAG) has been introduced. The current paper extends these latest findings by proposing a \textit{multi-input multi-output} (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.
SELM: Software Engineering of Machine Learning Models

One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning.
�Rules without a Reason�: ODEs in a concept image framework

Elementary ODEs are seen as prerequisite knowledge gained from the introductory calculus sequence for any physics student entering the upper division.In this paper, we provide evidence that while students might be well-versed in the rules and notations of ODEs, this does not necessarily translate to the application of these "rules without a reason" to novel physics tasks.Using the mathematics education researchers Tall and Vinner's concept image framework, we propose that the body of knowledge or concept image a student brings to an upper division physics environment regarding ODEs is restricted.We present, via four student interviews, three potential signals of this "restricted concept image": mathematical processes that are formally taught in introductory calculus and physics courses but are not reliably evoked when faced with a novel physics task.Our goal for this paper, as part of a larger project exploring student difficulties regarding ODEs, is to create a "proof of concept" that can be used in future work to more definitively identify the presence of a restricted ODE concept image.
Personas for supporting physicists' engagement in informal education

The pathways and engagement of physicists in informal physics education are varied, which makes their professional development needs not well understood.As part of ongoing efforts to build and support community in the informal physics space, we conducted interviews with physics practitioners and researchers with a range of different experiences.Through thematic analysis, we use personas methodology to articulate the needs and pain points of professional physicists.We present our set of four personas: the physicist who engages in informal physics for self-reflection, the physicist who wants to spark interest in physics, the physicist who wants to provide diverse role models to younger students and inspire them to pursue a STEM career, and the physicist who wants to improve the relationship between scientists and the public.This work will allow the informal physics community to create tailored resources for the variety of professional development needs of informal physics facilitators.
Making expert cognitive processes visible: planning and preliminary analysis in theoretical physics research

Many of the activities and cognitive processes that physicists use while solving problems are "invisible" to students, which can hinder their acquisition of important expert-like skills.Whereas the detailed calculations performed by researchers are often published in journals and textbooks, other activities such as those undertaken while planning how to approach a problem are rarely discussed in published research.Hence, these activities are especially hidden from students.To better understand how physicists solve problems in their professional research, we leveraged the framework of cognitive task analysis to conduct semi-structured interviews with theoretical physicists (N = 11).Here we elucidate the role of planning and preliminary analysis in theorists' work.Theorists described using a variety of activities in order to decide if their project was doable while also generating possible solution paths.These actions included doing preliminary calculations, reflecting on previous knowledge, gaining intuition and understanding by studying prior work, and reproducing previous results.We found that theorists typically did not pursue projects unless they had a clear idea of what the outcome of their project would be, or at least knew that they would be able to make progress on the problem.Thus, this preliminary design and analysis phase was highly important for theorists despite being largely hidden from students.We conclude by suggesting potential ways to incorporate our findings into the classroom to give students more numerous opportunities to engage in these expert-like practices.
PEDAGOGICAL CONDITIONS FOR THE FORMATION OF THE PROFESSIONAL READINESS OF FUTURE BORDER OFFICERS FOR PROJECT MANAGEMENT

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ARTIFICIAL INTELLIGENCE BASED CYBER- SECURITY PROGRAM

The modern world needs modern solution, Since IT Sector has Boomed over past 2 decades; It has made considerable amount of growth in it tech firms organize their data in their respective database which can be hacked (Cyber Threats).So, in order to avoid these, cyber these cyber-attacks the firms must ensure that there every minute or negligible bugs in their system.This can be done with the help of man force as well as AI (Artificial Intelligence).Due to the increasing rate of cyber-attacks, one must ensure that their System has improvised security system.Without substantial, automation, individual, cannot manage the complexity of operational and the scale of information to be utilized to secure the Cyberspace.This research paper provides incisive idea about the implementation of Artificial Intelligence for the better of Cyber -space well as Create a Healthy and secure environment to the user which will help them maintain their precious data up till their private tabs.This includes a brief description how we can make a fair usage of Artificial Intelligence to secure our data Systems.We may infer that heirloom application already exits after the review of current AI technology on cybersecurity.
The Internet of Things Security and Privacy: Current Schemes, Challenges and Future Prospects

The Internet of Things devices and users exchange massive amount of data. Some of these exchanged messages are highly sensitive as they involve organizational, military or patient personally identifiable information. Therefore, many schemes and protocols have been put forward to protect the transmitted messages. The techniques deployed in these schemes may include blockchain, public key infrastructure, elliptic curve cryptography, physically unclonable function and radio frequency identification. In this paper, a review is provided of these schemes including their strengths and weaknesses. Based on the obtained results, it is clear that majority of these protocols have numerous security, performance and privacy issues.
Patentability of Computer Program Algorithms in the G20 States

Ubiquitous computerization and digitalization are contributing to the unprecedented growth of the software market. Computer programs are protected as subject of copyright law in international law and domestic legal systems. However, copyright law does not protect the interests of the copyright holder from borrowing ideas and algorithms which often have agreat commercial value. This circumstance has prompted the legal science and law enforcement practice of the most developed states to justify the possibility of protecting computer programs and their algorithms. The leading states chosen for in this paper are the G20 states. The relevance of this choice is due to the following: 1) The G20 states account for 86% of global GDP; 2) All world leaders in computer software development are G20 members; 3) All BRICS states are G20 members; 4) The law-and-orders of the G20 states are relevant to all existing traditions of the legal protection of intellectual property in the world. The legal systems of the G20 states follow one of three approaches according to the criterion of patentability of computer programs and their algorithms. We call the first approach “neutral.” It includes States which legislation does not explicitly prohibit the patenting of computer programs, but computer programs themselves are not mentioned among the subject matters of inventions. The second (“positive”) approach includes those states which legislation explicitly classifies computer programs as patentable inventions. On the contrary, the third (“negating”) approach includes states where it is legally established that computer programs as such are unpatentable. The results of the research demonstrate that there is no direct correlation between the way of solving the issue of patentability of computer program algorithms in different legal systems and the state’s place in the global IT market. For example, the United States and China take aneutral approach, Japan takes apositive approach, the EU Member States and India take anegating approach. We believe that the most flexible approach is aneutral approach from the point of view of patent law policy. The most liberal and consistent approach is the positive approach presented by the Japanese legal system. Finally, the negating approach is the most controversial and at the same time widespread among the G20 and BRICS states.
Social Simulation as a Prognostic Tool for Communication Processes: Theoretical and Philosophical Perspectives

The article presents social simulation from theoretical and philosophical perspectives as a prognostic tool for researching, analysing and anticipating communication and other processes in social environments. The first part discusses the phenomena of ontological and epistemological simulation, treating social simulation processes as epistemological ones. The second part analyses the attitude of the French sociologist and media philosopher Jean Baudrillard towards social simulation, which he himself treats as ontological one. The counterarguments to introduce Baudrillard’s unidentified distinction between ontological and epistemological simulation processes are presented. The third part deals with the principles of the functioning of social simulations as a prognostic tool and provides analytical possibilities for various social environments, communication and other processes. At the end of the article, it is concluded that Baudrillard’s concept of ontological simulation, which annihilates classical Western metaphysics, erroneously reduces the difference between ontos and epistēmē.
Assisted Specification of Code Using Search

We describe an intelligent assistant based on mining existing software repositories to help the developer interactively create checkable specifications of code. To be most useful we apply this at the subsystem level, that is chunks of code of 1000-10000 lines that can be standalone or integrated into an existing application to provide additional functionality or capabilities. The resultant specifications include both a syntactic description of what should be written and a semantic specification of what it should do, initially in the form of test cases. The generated specification is designed to be used for automatic code generation using various technologies that have been proposed including machine learning, code search, and program synthesis. Our research goal is to enable these technologies to be used effectively for creating subsystems without requiring the developer to write detailed specifications from scratch.
Teaching agile operation and leadership through linked university courses

Agile software development methods, especially Scrum, are commonly used in software development companies. For this reason, our goal was that our undergraduate students gain experience as Scrum development team members and our master's students as agile leaders. To this end, we had redesigned and linked an undergraduate and a master's course, and launched the new course in the spring of 2021. The success of our approach was confirmed by a questionnaire survey of 86 undergraduate and 27 master's students. A/B testing was also performed. Our approach is a novelty compared to solutions where the Scrum Master is a course member, an instructor, or a university employee. In addition to being resource-efficient, it also offers master's students an unparalleled opportunity to develop agile leadership skills.&#x0D; Subject Classification: 97U50
Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models

Testing black-box perceptual-control systems in simulation faces two difficulties. Firstly, perceptual inputs in simulation lack the fidelity of real-world sensor inputs. Secondly, for a reasonably accurate perception system, encountering a rare failure trajectory may require running infeasibly many simulations. This paper combines perception error models -- surrogates for a sensor-based detection system -- with state-dependent adaptive importance sampling. This allows us to efficiently assess the rare failure probabilities for real-world perceptual control systems within simulation. Our experiments with an autonomous braking system equipped with an RGB obstacle-detector show that our method can calculate accurate failure probabilities with an inexpensive number of simulations. Further, we show how choice of safety metric can influence the process of learning proposal distributions capable of reliably sampling high-probability failures.
Comparing Shape-Constrained Regression Algorithms for Data Validation

Industrial and scientific applications handle large volumes of data that render manual validation by humans infeasible. Therefore, we require automated data validation approaches that are able to consider the prior knowledge of domain experts to produce dependable, trustworthy assessments of data quality. Prior knowledge is often available as rules that describe interactions of inputs with regard to the target e.g. the target must be monotonically decreasing and convex over increasing input values. Domain experts are able to validate multiple such interactions at a glance. However, existing rule-based data validation approaches are unable to consider these constraints. In this work, we compare different shape-constrained regression algorithms for the purpose of data validation based on their classification accuracy and runtime performance.
Non-Disclosing Credential On-chaining for Blockchain-based Decentralized Applications

Many service systems rely on verifiable identity-related information of their users. Manipulation and unwanted exposure of this privacy-relevant information, however, must at the same time be prevented and avoided. Peer-to-peer blockchain-based decentralization with a smart contract-based execution model and verifiable off-chain computations leveraging zero-knowledge proofs promise to provide the basis for next-generation, non-disclosing credential management solutions. In this paper, we propose a novel credential on-chaining system that ensures blockchain-based transparency while preserving pseudonymity. We present a general model compliant to the W3C verifiable credential recommendation and demonstrate how it can be applied to solve existing problems that require computational identity-related attribute verification. Our zkSNARKs-based reference implementation and evaluation show that, compared to related approaches based on, e.g., CL-signatures, our approach provides significant performance advantages and more flexible proof mechanisms, underpinning our vision of increasingly decentralized, transparent, and trustworthy service systems.
Stable Alert Decision Method and Machine Learning based P-Wave Detection for On-Site Earthquake Early Warning

Abstract Proactively responding to earthquakes is challenging. Researchers have developed methods to reduce structural damage from earthquakes. The Earthquake Early Warning (EEW) service is more effective than seismic design at reducing human casualties. We developed an on-site EEW that can provide alerts faster than the network-based EEW. However, on-site EEW is not used as a public service due to the low alarm accuracy of the existing on-site alarm. Therefore, we applied both methods to improve the alarming accuracy of on-site EEW. First, the developed system increased the accuracy of the identification rate for the initial P-wave using deep learning. Second, the method utilizing a nearby seismometer was added to improve the accuracy of the alarm. We conducted an on-site EEW trial operation and checked the performance from 2020.10.08 to 2022.03.31. We reviewed the warning cases for small and large events during the trial and confirmed the possibility of automated alert decisions.
Chaotic guided local search algorithm for solving optimization problems

Abstract Chaos optimization algorithm (COA) can be an interesting alternative in a global optimization problem. Due to the non-repetition and ergodicity of chaos, it can explore the global search space at higher speeds than stochastic searches that depend on probabilities. To adjust the solution obtained by COA, guided local search algorithm (GLS) is integrated with COA to form a hybrid algorithm. The results show that the present algorithms significantly outperforms the existing methods in terms of con- vergence speed, numerical stability and a better optimal solution than other algorithms.
Frequency Dropout: Feature-Level Regularization via Randomized Filtering

Deep convolutional neural networks have shown remarkable performance on various computer vision tasks, and yet, they are susceptible to picking up spurious correlations from the training signal. So called `shortcuts' can occur during learning, for example, when there are specific frequencies present in the image data that correlate with the output predictions. Both high and low frequencies can be characteristic of the underlying noise distribution caused by the image acquisition rather than in relation to the task-relevant information about the image content. Models that learn features related to this characteristic noise will not generalize well to new data. In this work, we propose a simple yet effective training strategy, Frequency Dropout, to prevent convolutional neural networks from learning frequency-specific imaging features. We employ randomized filtering of feature maps during training which acts as a feature-level regularization. In this study, we consider common image processing filters such as Gaussian smoothing, Laplacian of Gaussian, and Gabor filtering. Our training strategy is model-agnostic and can be used for any computer vision task. We demonstrate the effectiveness of Frequency Dropout on a range of popular architectures and multiple tasks including image classification, domain adaptation, and semantic segmentation using both computer vision and medical imaging datasets. Our results suggest that the proposed approach does not only improve predictive accuracy but also improves robustness against domain shift.
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL

The cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications. Unfortunately, the theoretical understanding of this MARL problem is lacking due to the curse of many agents and the limited exploration of the relational reasoning in existing works. In this paper, we verify that the transformer implements complex relational reasoning, and we propose and analyze model-free and model-based offline MARL algorithms with the transformer approximators. We prove that the suboptimality gaps of the model-free and model-based algorithms are independent of and logarithmic in the number of agents respectively, which mitigates the curse of many agents. These results are consequences of a novel generalization error bound of the transformer and a novel analysis of the Maximum Likelihood Estimate (MLE) of the system dynamics with the transformer. Our model-based algorithm is the first provably efficient MARL algorithm that explicitly exploits the permutation invariance of the agents. Our improved generalization bound may be of independent interest and is applicable to other regression problems related to the transformer beyond MARL.
Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection

Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new classes or new domains. A central challenge in Continual Learning is overcoming the effects of catastrophic forgetting, which refers to the sudden drop in accuracy on previously learned tasks after the model is trained on new classes or domains. In continual classification this challenge is often overcome by replaying a small selection of samples from previous tasks, however replay is rarely considered in CSS. Therefore, we investigate the influences of various replay strategies for semantic segmentation and evaluate them in class- and domain-incremental settings. Our findings suggest that in a class-incremental setting, it is critical to achieve a uniform distribution for the different classes in the buffer to avoid a bias towards newly learned classes. In the domain-incremental setting, it is most effective to select buffer samples by uniformly sampling from the distribution of learned feature representations or by choosing samples with median entropy. Finally, we observe that the effective sampling methods help to decrease the representation shift significantly in early layers, which is a major cause of forgetting in domain-incremental learning.