Reviewerly/exHarmony huggingface dataset:
Safe AI -- How is this Possible?

Ttraditional safety engineering is coming to a turning point moving from deterministic, non-evolving systems operating in well-defined contexts to increasingly autonomous and learning-enabled AI systems which are acting in largely unpredictable operating contexts. We outline some of underlying challenges of safe AI and suggest a rigorous engineering framework for minimizing uncertainty, thereby increasing confidence, up to tolerable levels, in the safe behavior of AI systems.
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial  Robustness

This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings. In our work, we replace the conventional ReLU-based nonlinearities with blocks comprising locally and stochastically competing linear units. The output of each network layer now yields a sparse output, depending on the outcome of winner sampling in each block. We rely on the Variational Bayesian framework for training and inference; we incorporate conventional PGD-based adversarial training arguments to increase the overall adversarial robustness. As we experimentally show, the arising networks yield state-of-the-art robustness against powerful adversarial attacks while retaining very high classification rate in the benign case.
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural  Networks

This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs. Then, we combine the results of these runs to obtain the final result. We prove that DropGNNs can distinguish various graph neighborhoods that cannot be separated by message passing GNNs. We derive theoretical bounds for the number of runs required to ensure a reliable distribution of dropouts, and we prove several properties regarding the expressive capabilities and limits of DropGNNs. We experimentally validate our theoretical findings on expressiveness. Furthermore, we show that DropGNNs perform competitively on established GNN benchmarks.
Pedagogical Implications of Stancetaking and Identity Construction in Lecturer-Student Interaction

During a consultation at the postgraduate level, interactions between lecturer and student are essential in completing a thesis or dissertation.In most interactions, both speakers tend to construct their identities with their stance.Consequently, this paper examines how the postgraduate lecturers and students take a stance and construct their identities in lecturer-student interaction.Moreover, it explores the pedagogical implications of conversational stance and identity construction.This study combined the Stance Triangle and Stance Marker as a theoretical framework to analyze the construction of identity between lecturers and students and employed conversation analysis as an approach in the data analysis.Ten conversations between lecturer and postgraduate students during consultations were examined.The findings of the study reveal that attitudinal, deontic, epistemic, and textual stance markers are frequently used and linked to how they construct their identities.Results further show that lecturers position themselves as mentors, experts, counselors, and leaders, while the students position themselves as mentees, non-experts, counselees, and followers.Such diverse identities may impact the lecturer-student relationship and students' academic performance.In addition, it provides opportunities for lecturers to enhance their supervisory skills and strategies and develop better classroom interaction.
Augmented Reality

Augmented Reality (AR) interfaces have happened intentional widely over the last few decades, accompanying a increasing number of consumer-located experiments. In this paper, we orderly review 10 age of ultimate effective AR consumer studies, from 2005 to 2014. A total of 291 documents accompanying 369 individual consumer studies have existed inspected and top-secret established their request fields. The basic offering of the review search out present the broad countryside of consumer-located AR research, and to determine a high-ranking view of by what method that countryside has exchanged. We rehash the high-ranking gifts each type of documents, and present instances of ultimate powerful consumer studies. We again label fields place skilled have happened few consumer studies, and convenience for future research. Among other things, we find that skilled is a increasing flow toward handheld AR consumer studies, what most studies are administered in workshop backgrounds and do not include ship experiment. This research will suffice for AR scientists the one be going to attend best practices in plotting their own AR consumer studies.
Semantic Interaction in Augmented Reality Environments for Microsoft HoloLens

Augmented Reality is a promising technique for human-machine interaction. Especially in robotics, which always considers systems in their environment, it is highly beneficial to display visualizations and receive user input directly in exactly that environment. We explore this idea using the Microsoft HoloLens, with which we capture indoor environments and display interaction cues with known object classes. The 3D mesh recorded by the HoloLens is annotated on-line, as the user moves, with semantic classes using a projective approach, which allows us to use a state-of-the-art 2D semantic segmentation method. The results are fused onto the mesh; prominent object segments are identified and displayed in 3D to the user. Finally, the user can trigger actions by gesturing at the object. We both present qualitative results and analyze the accuracy and performance of our method in detail on an indoor dataset.
Moderating role of turbulent environment between IT dynamic capabilities, business agility, and innovative capabilities: Empirical evidence from Sri Lanka

Drawing on the resource-based theory and dynamic capability theory this study examines how IT-enabled dynamic capabilities impact on business capabilities (business process agility and firm innovative capability) with moderating role of turbulent environment. The purpose of this study is to test the impact of ITenabled dynamic on business process agility and innovative capability that in turn drive firm performance under turbulent environment. This study conceptualizes that IT governance mechanism positively effect on ITDC, that in turn positively impact on business process agility and firm innovative capability to achieve firm performance. Data were collected in offline and online by using convenient and snowball sampling technique from the senior IT and business managers in Sri Lankan. Using the partial least squares (Smart PLS 3.0) hierarchical regression analysis for various model were performed. The finding from 188 senior IT and business manager’s responses from Sri Lanka reveals the strong and positive relationship in the hypothesized relationship. In contrary to the expectation, the turbulent environment’s both hypotheses (H6a and H6b) failed to show a significant moderating effect. This study adds to the existing literature and extends the managerial practice by focusing on the key constructs such as IT governance mechanism, ITDC, turbulent environment, and firm performance.
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently

Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model's predictions. However, the transferability has not been understood in terms of to which class target model's predictions were misled (i.e., class-aware transferability). In this paper, we differentiate the cases in which a target model predicts the same wrong class as the source model ("same mistake") or a different wrong class ("different mistake") to analyze and provide an explanation of the mechanism. We find that (1) AEs tend to cause same mistakes, which correlates with "non-targeted transferability"; however, (2) different mistakes occur even between similar models, regardless of the perturbation size. Furthermore, we present evidence that the difference between same mistakes and different mistakes can be explained by non-robust features, predictive but human-uninterpretable patterns: different mistakes occur when non-robust features in AEs are used differently by models. Non-robust features can thus provide consistent explanations for the class-aware transferability of AEs.
The main aspects of teaching in the context of the development of the educational environment and culture at universities

The main purpose of the article is to study the main aspects of teaching in the context of the development of the educational environment and culture at universities. At all times and in all cultural traditions, there was a special respect for the teacher. After all, a teacher is not only a carrier and distributor of knowledge, but also an educator of value and moral guidelines in a person's life. Thus, it can be said that a teacher/educator/ is a kind of "educator of the soul" of a person. With this in mind, it is the role of the teacher that is key in the process of organizing the educational environment and culture at the university. For this, general theoretical and specific methods of theoretical research of the most advanced literary sources were used. In addition, a survey was conducted among students of a higher educational institution about the role a teacher plays in the process of organizing the educational environment and culture at the university. As a result of the study, the main aspects of teaching in the context of the development of the educational environment and culture at universities were analyzed and systematized.
Spectral Algorithms Optimally Recover Planted Sub-structures

Spectral algorithms are an important building block in machine learning and graph algorithms. We are interested in studying when such algorithms can be applied directly to provide optimal solutions to inference tasks. Previous works by Abbe, Fan, Wang and Zhong (2020) and by Dhara, Gaudio, Mossel and Sandon (2022) showed the optimality for community detection in the Stochastic Block Model (SBM), as well as in a censored variant of the SBM. Here we show that this optimality is somewhat universal as it carries over to other planted substructures such as the planted dense subgraph problem and submatrix localization problem, as well as to a censored version of the planted dense subgraph problem.
Extensions of the Mandelstam-Tamm quantum speed limit to systems in  mixed states

The Mandelstam-Tamm quantum speed limit puts a bound on how fast a closed system in a pure state can evolve. In this paper, we derive several extensions of this quantum speed limit to closed systems in mixed states. We also compare the strengths of these extensions and examine their tightness. The most widely used extension of the Mandelstam-Tamm quantum speed limit originates in Uhlmann's energy dispersion estimate. We carefully analyze the underlying geometry of this estimate, an analysis that makes apparent that the Bures metric, or equivalently the quantum Fisher information, will rarely give rise to tight extensions. This observation leads us to address whether there is a tightest general extension of the Mandelstam-Tamm quantum speed limit. Using a geometric construction similar to that developed by Uhlmann, we prove that this is indeed the case. In addition, we show that tight evolutions of mixed states are typically generated by time-varying Hamiltonians, which contrasts with the case for systems in pure states.
Evaluation of Distributed Data Processing Frameworks in Hybrid Clouds

Distributed data processing frameworks (e.g., Hadoop, Spark, and Flink) are widely used to distribute data among computing nodes of a cloud. Recently, there have been increasing efforts aimed at evaluating the performance of distributed data processing frameworks hosted in private and public clouds. However, there is a paucity of research on evaluating the performance of these frameworks hosted in a hybrid cloud, which is an emerging cloud model that integrates private and public clouds to use the best of both worlds. Therefore, in this paper, we evaluate the performance of Hadoop, Spark, and Flink in a hybrid cloud in terms of execution time, resource utilization, horizontal scalability, vertical scalability, and cost. For this study, our hybrid cloud consists of OpenStack (private cloud) and MS Azure (public cloud). We use both batch and iterative workloads for the evaluation. Our results show that in a hybrid cloud (i) the execution time increases as more nodes are borrowed by the private cloud from the public cloud, (ii) Flink outperforms Spark, which in turn outperforms Hadoop in terms of execution time, (iii) Hadoop transfers the largest amount of data among the nodes during the workload execution while Spark transfers the least amount of data, (iv) all three frameworks horizontally scale better as compared to vertical scaling, and (v) Spark is found to be least expensive in terms of $ cost for data processing while Hadoop is found the most expensive.
Motion Planning for Redundant Articulated Robots Based on Geometrical Properties of the Whole Robot Body in Initial and Goal Configuration

This paper discusses motion planning techniques for redundant articulated robots. Motion planning in this paper means to generate joint trajectories that can avoid obstacles. In the proposed motion planning method, candidate waypoints are generated from the overall shape of the initial and goal configurations. All procedures of the proposed method are constructed only by computing inverse kinematics, and it does not require path planning in joint space. The effectiveness of the proposed method is verified through performance evaluation test using a 7-DOF manipulator. In the performance evaluation test, not only the trajectory length and calculation time, but also the risk of collision with obstacles that are not considered in the simulator are evaluated.
Learning Multiple Dense Prediction Tasks from Partially Annotated Data

Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient approach and look at jointly learning of multiple dense prediction tasks on partially annotated data, which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. In particular, we learn to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs, and avoids learning trivial cross-task relations by retaining high-level information about the input image. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.
Multi-winner Approval Voting Goes Epistemic

Epistemic voting interprets votes as noisy signals about a ground truth. We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper bound on its cardinality. A prototypical problem for this setting is the aggre-gation of multi-label annotations with prior knowledge on the size of the ground truth. We posit noisemodels, for which we define rules that output an optimal set of winners. We report on experiments on multi-label annotations (which we collected).
An enhanced framework for solving cold start problem in movie recommendation systems

&lt;p&gt;Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N and precision@N and it is applied to 1M, 10M and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791 and 0.8739.&lt;/p&gt;
"The punishment imposed by nature, or the Ghostliness of Everyday life in the image of E. T. A. Hoffman (based on the novel "The Sinister Guest")"

The relevance of the research lies in the scientific significance of the problem of studying the main plot in the works of E. T. A. Hoffman, manifested at the level of a group of works in which demonological personalities (ghosts / ghosts, vampires, magnetizers) act. The general invariant plot-thematic scheme of short stories ("Magnetizer", "Ignaz Denner", "Ghost Story", "Entail", etc.) is associated with the image of the intervention of otherworldly forces in real life, which is given the character of ghostliness. The object of the study is the short story "The Sinister Guest" ("Der unheimliche Gast", 1819), the plot of which tells about the impact of a "hostile beginning" on a person. The subject of the study is one of the artistic embodiments of Hoffmann's "alien spiritual principle". It is shown that the "sinister guest", the hero of the novel of the same name, is a product of Nature, avenging a crime and an attempt to interfere in its secrets; people are also punished for their unwillingness to live in harmony with it. At the same time, the right moral choice helps the heroes of this novel to avoid the destructive power of the "sinister guest". The novelty of the study is seen in the fact that when analyzing the story of a villain endowed with mysterious knowledge, a deviation from the main plot associated with the specific situation of the novel "The Sinister Guest" is considered. This approach made it possible to clarify the understanding of the duality of being by E. T. A. Hoffman, expressed through motifs (sound, portrait, dream, mystery, affinity of souls) and natural symbols (lightning / thunderstorms, spider, elder), as well as the idea of universal dependence of people on each other and on Nature.
Study of the Possibility of Improving the Efficiency of the Certification Process of the Information Object

Attestation of information objects (IO) is an important stage in the implementation of information protection system at enterprise, organization or state body. In essence, attestation is a set of measures carried out at the IO to check the reliability of the implemented information protection system, as a result of which compliance with certain requirements of the state regulator in the field of technical information protection (FSTEC of Russia) is confirmed. The attestation process is necessary as a stage preceding the beginning of processing of the protected information, which provides for comprehensive (certification) tests of the protected IO in order to assess them. Such assessment is carried out in terms of compliance of the set of means and security measures used in IO to the put forward level of protection and should be carried out under real operating conditions of the system. The “Certificate of Conformity” obtained as a result of attestation gives the right to processes of information processing with the established level of confidentiality for the period specified in the certificate. This work examines various aspects of attestation of information object, the structure of attestation tests, legal framework, and organizational issues. The basic stages of attestation tests, and a list of necessary reporting documentation formed at various stages of tests are allocated. The concept of evaluation of attestation tests’ efficiency is considered and the question of the approach’s realization to increase the efficiency of information object’s attestation of information security requirements is raised.
Emphasis on the Minimization of False Negatives or False Positives in Binary Classification

The minimization of specific cases in binary classification, such as false negatives or false positives, grows increasingly important as humans begin to implement more machine learning into current products. While there are a few methods to put a bias towards the reduction of specific cases, these methods aren't very effective, hence their minimal use in models. To this end, a new method is introduced to reduce the False Negatives or False positives without drastically changing the overall performance or F1 score of the model. This method involving the careful change to the real value of the input after pre-training the model. Presenting the results of this method being applied on various datasets, some being more complex than others. Through experimentation on multiple model architectures on these datasets, the best model was found. In all the models, an increase in the recall or precision, minimization of False Negatives or False Positives respectively, was shown without a large drop in F1 score.
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning

Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data. For low-resource scenarios, prompt-based learning for PLMs exploits prompts as task guidance and turns downstream tasks into masked language problems for effective few-shot fine-tuning. In most existing approaches, the high performance of prompt-based learning heavily relies on handcrafted prompts and verbalizers, which may limit the application of such approaches in real-world scenarios. To solve this issue, we present CP-Tuning, the first end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. We further propose the pair-wise cost-sensitive contrastive learning procedure to optimize the model in order to achieve verbalizer-free class mapping and enhance the task-invariance of prompts. It explicitly learns to distinguish different classes and makes the decision boundary smoother by assigning different costs to easy and hard cases. Experiments over a variety of language understanding tasks used in IR systems and different PLMs show that CP-Tuning outperforms state-of-the-art methods.