Reviewerly/exHarmony huggingface dataset:
A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness

The recent advancement in Multimedia Analytical, Computer Vision (CV), and Artificial Intelligence (AI) algorithms resulted in several interesting tools allowing an automatic analysis and retrieval of multimedia content of users' interests. However, retrieving the content of interest generally involves analysis and extraction of semantic features, such as emotions and interestingness-level. The extraction of such meaningful information is a complex task and generally, the performance of individual algorithms is very low. One way to enhance the performance of the individual algorithms is to combine the predictive capabilities of multiple algorithms using fusion schemes. This allows the individual algorithms to complement each other, leading to improved performance. This paper proposes several fusion methods for the media interestingness score prediction task introduced in CLEF Fusion 2022. The proposed methods include both a naive fusion scheme, where all the inducers are treated equally and a merit-based fusion scheme where multiple weight optimization methods are employed to assign weights to the individual inducers. In total, we used six optimization methods including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Nelder Mead, Trust Region Constrained (TRC), and Limited-memory Broyden Fletcher Goldfarb Shanno Algorithm (LBFGSA), and Truncated Newton Algorithm (TNA). Overall better results are obtained with PSO and TNA achieving 0.109 mean average precision at 10. The task is complex and generally, scores are low. We believe the presented analysis will provide a baseline for future research in the domain.
A Federated Cox Model with Non-Proportional Hazards

Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice. Neural networks, however, typically rely on data that are centrally available, whereas healthcare data are frequently held in secure silos. We present a federated Cox model that accommodates this data setting and also relaxes the proportional hazards assumption, allowing time-varying covariate effects. In this latter respect, our model does not require explicit specification of the time-varying effects, reducing upfront organisational costs compared to previous works. We experiment with publicly available clinical datasets and demonstrate that the federated model is able to perform as well as a standard model.
SALSA: Attacking Lattice Cryptography with Transformers

Currently deployed public-key cryptosystems will be vulnerable to attacks by full-scale quantum computers. Consequently, "quantum resistant" cryptosystems are in high demand, and lattice-based cryptosystems, based on a hard problem known as Learning With Errors (LWE), have emerged as strong contenders for standardization. In this work, we train transformers to perform modular arithmetic and combine half-trained models with statistical cryptanalysis techniques to propose SALSA: a machine learning attack on LWE-based cryptographic schemes. SALSA can fully recover secrets for small-to-mid size LWE instances with sparse binary secrets, and may scale to attack real-world LWE-based cryptosystems.
A description of Turkish Discourse Bank 1.2 and an examination of common dependencies in Turkish discourse

We describe Turkish Discourse Bank 1.2, the latest version of a discourse corpus annotated for explicitly or implicitly conveyed discourse relations, their constitutive units, and senses in the Penn Discourse Treebank style. We present an evaluation of the recently added tokens and examine three commonly occurring dependency patterns that hold among the constitutive units of a pair of adjacent discourse relations, namely, shared arguments, full embedding and partial containment of a discourse relation. We present three major findings: (a) implicitly conveyed relations occur more often than explicitly conveyed relations in the data; (b) it is much more common for two adjacent implicit discourse relations to share an argument than for two adjacent explicit relations to do so; (c) both full embedding and partial containment of discourse relations are pervasive in the corpus, which can be partly due to subordinator connectives whose preposed subordinate clause tends to be selected together with the matrix clause rather than being selected alone. Finally, we briefly discuss the implications of our findings for Turkish discourse parsing.
Information-Theoretic Bounds for Steganography in Multimedia

Steganography in multimedia aims to embed secret data into an innocent looking multimedia cover object. This embedding introduces some distortion to the cover object and produces a corresponding stego object. The embedding distortion is measured by a cost function that determines the detection probability of the existence of the embedded secret data. A cost function related to the maximum embedding rate is typically employed to evaluate a steganographic system. In addition, the distribution of multimedia sources follows the Gibbs distribution which is a complex statistical model that restricts analysis. Thus, previous multimedia steganographic approaches either assume a relaxed distribution or presume a proposition on the maximum embedding rate and then try to prove it is correct. Conversely, this paper introduces an analytic approach to determining the maximum embedding rate in multimedia cover objects through a constrained optimization problem concerning the relationship between the maximum embedding rate and the probability of detection by any steganographic detector. The KL-divergence between the distributions for the cover and stego objects is used as the cost function as it upper bounds the performance of the optimal steganographic detector. An equivalence between the Gibbs and correlated-multivariate-quantized-Gaussian distributions is established to solve this optimization problem. The solution provides an analytic form for the maximum embedding rate in terms of the WrightOmega function. Moreover, it is proven that the maximum embedding rate is in agreement with the commonly used Square Root Law (SRL) for steganography, but the solution presented here is more accurate. Finally, the theoretical results obtained are verified experimentally.
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.
Human-Centric Research for NLP: Towards a Definition and Guiding Questions

With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes research human-centric? We address this question by providing a working definition and define how a research pipeline can be split into different stages in which human-centric components can be added. Additionally, we discuss existing NLP with HCR components and define a series of guiding questions, which can serve as starting points for researchers interested in exploring human-centric research approaches. We hope that this work would inspire researchers to refine the proposed definition and to pose other questions that might be meaningful for achieving HCR.
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning

Sparse tensors are rapidly becoming critical components of modern deep learning workloads. However, developing high-performance sparse operators can be difficult and tedious, and existing vendor libraries cannot satisfy the escalating demands from new operators. Sparse tensor compilers simplify the development of operators, but efficient sparse compilation for deep learning remains challenging because a single sparse format cannot maximize hardware efficiency, and single-shot compilers cannot keep up with latest hardware and system advances. In this paper, we observe that the key to addressing both these challenges is to leverage composable formats and composable transformations. We propose SparseTIR, a sparse tensor compilation abstraction that offers composable formats and composable transformations for deep learning workloads. SparseTIR constructs a search space over these composable components for performance tuning. With these improvements, SparseTIR obtains consistent performance speedups vs vendor libraries on GPUs for single operators: 1.20-2.34x for GNN operators, 1.05-2.98x for sparse attention operators, and 0.56-7.45x for sparse convolution operators. SparseTIR also accelerates end-to-end GNNs by 1.08-1.52x for GraphSAGE training, and 4.20-40.18x for RGCN inference.
Syzygies for the vector invariants of the dihedral group

The problem of finding generators of the $GL$-ideal of the relations between the generators of the algebra of invariants of the dihedral group acting on $m$-tuples of vectors from its defining $2$-dimensional representation is studied. It is shown that this $GL$-ideal is generated by relations depending on no more than $3$ vector variables. A minimal $GL$-ideal generating system is found for the case when $m=2$, and for the case of the dihedral group of order $8$ and arbitrary $m$.
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data

Errors might not have the same consequences depending on the task at hand. Nevertheless, there is limited research investigating the impact of imbalance in the contribution of different features in an error vector. Therefore, we propose the Feature Impact Balance (FIB) score. It measures whether there is a balanced impact of features in the discrepancies between two vectors. We designed the FIB score to lie in [0, 1]. Scores close to 0 indicate that a small number of features contribute to most of the error, and scores close to 1 indicate that most features contribute to the error equally. We experimentally study the FIB on different datasets, using AutoEncoders and Variational AutoEncoders. We show how the feature impact balance varies during training and showcase its usability to support model selection for single output and multi-output tasks.
Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox

Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation. Its main challenge lies in how to identify target samples in unshared or unknown classes. Previous methods commonly strive to depict sample "confidence" along with a threshold for rejecting unknowns, and align feature distributions of shared classes across domains. However, it is still hard to pre-specify a "confidence" criterion and threshold which are adaptive to various real tasks, and a mis-prediction of unknowns further incurs misalignment of features in shared classes. In this paper, we propose a new UniDA method with adaptive Unknown Authentication by Classifier Paradox (UACP), considering that samples with paradoxical predictions are probably unknowns belonging to none of the source classes. In UACP, a composite classifier is jointly designed with two types of predictors. That is, a multi-class (MC) predictor classifies samples to one of the multiple source classes, while a binary one-vs-all (OVA) predictor further verifies the prediction by MC predictor. Samples with verification failure or paradox are identified as unknowns. Further, instead of feature alignment for shared classes, implicit domain alignment is conducted in output space such that samples across domains share the same decision boundary, though with feature discrepancy. Empirical results validate UACP under both open-set and universal UDA settings.
An optimal MOO strategy

We calculated a fixed strategy that minimizes the average number of guesses (minimum strategy) for the number-guessing game MOO by exhaustive search. Although the minimum strategy for a similar game, mastermind, has been reported, this study seems to be the first to find the minimum strategy for MOO with a larger search space. When two players play against each other in MOO, the minimum strategy is not always the strongest fixed strategy. First, we compute a fixed strategy that has the maximum winning rate when played against the minimum strategy. Then we confirm that there is no fixed strategy with a winning rate exceeding 0.5 against this strategy. This result shows that MOO is a game with the strongest fixed strategy.
On Grid Codes

Versions of the Hamming and Gilbert-Varshamov bounds for codes in $\prod_{i=1}^{n}[0,m_{i}-1]$ with respect to the Manhattan distance are presented. Given an abelian group $G$ isomorphic to $C_{m_{1}}\times \cdots \times C_{m_{n}}$, the Hamming, Manhattan, and Lee distances are defined in $G$; a formula for the minimum Hamming distance of codes that are cyclic subgroups of $G$ is provided, and some lower bounds for the minimum Manhattan distance of these codes are determined in terms of their minimum Hamming and Lee distances. Examples illustrating the main results and an application of these are provided.
Decision Boundary to Improve the Sensitivity of Deep Neural Networks Models

In spite of their performance and relevance on various image classification fields, deep neural network classifiers encounter real difficulties face of minor information perturbations. In particular, the presence of contradictory examples causes a big weakness and insufficiency of deep learning models in many areas, such as illness recognition. The aim of our paper is to improve the robustness of deep neural network models to small input perturbations using standard training and adversial training to maximize the distance between predict instances and the boundary decision area. We shows the decision boundary performance of deep neural networks during model training, the minimum distance of the input images from the decision boundary area and how this distance develops during the deep neural network training. The results shows that the distance between the images and the decision boundary decreases during standard training. However, adversarial training increases this distance, which improve the performance of our model. Our work presents a new solution to the deep neural networks sensitivity problem. We found a very strong relationship between the efficiency of the deep neural networks model and the training phase. We can say that the efficiency is created during training, it is not predetermined by the initialization or architecture.
THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) IN HIGHER EDUCATION: ADVANTAGES AND DISADVANTAGES

The article presents the research into the specifics of applying information and communication technology (ICT) in the academic environment of higher education institutions.The aim of the article is to identify the advantages and disadvantages of using modern information and communication technology.The research is meant to contribute to improving the activities of teachers and students, as well as the organization of higher education institutions' functioning in general.In the course of the research the analysis of scientific and pedagogical literature was performed, which highlighted various aspects of the research problem.It was found that the main areas of research are the informatization of education (V.Bykov, M. Shyshkina, O. Spirin); formation of ICT competence of specialists (M.Kademiya, N. Morze, I. Vorotnykova); ensuring the quality of ICTbased education (Ju.Zaporozhchenko, V. Osadchyi); the use of ICT as a learning and teaching tool (Koryahin, N. Mukan, O. Blavt, V. Virt, Ju.Lavrysh).The main advantages of applying ICT in the academic environment of higher education institutions are identified, namely: reducing the students' workload; improving the process of editing and making changes; improving the communication process; transforming the education system; generating ideas to solve problems; providing support for lifelong learning; improving the evaluation system and procedures; increasing effectiveness and efficiency; developing technology to improve performance of management functions.The main shortcomings of applying ICT in education are highlighted: vague statement of the problem to be solved; lack of clear and general indicators and criteria according to which innovations in the field of education should be implemented; lack of flexibility in education policy; lack of appropriate technical equipment and software; lack or insufficient level of knowledge, skills and abilities of participants in the educational process, their psychological unpreparedness for the use of ICT in everyday educational practice; insufficient level of teachers' competence in using ICT; insufficient level of funding.Conclusions are presented stating that at the beginning of the XXI century there exist a number of factors that justify the need for new ideas and the use of ICT in many spheres of society, including education.Their advantages and disadvantages need further studies the same as \clearly defined recommendations on application of ICT in higher education and further dissemination of best practices require more time and effort.
Simulations of quantum nonlocality with local negative bits

We propose a simple simulation of nonlocal quantum correlations among $N$ qubits using a local hidden variable source with a positive probability distribution, given that each the $N$ observers has access to a local negative bit. Notably, unlike the Toner-Bacon protocol, no exchange of classical bits between the observers is required. Moreover, our simulation can be extended to include Popescu-Rohrlich box correlations.
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval

Multi-hop reasoning (i.e., reasoning across two or more documents) is a key ingredient for NLP models that leverage large corpora to exhibit broad knowledge. To retrieve evidence passages, multi-hop models must contend with a fast-growing search space across the hops, represent complex queries that combine multiple information needs, and resolve ambiguity about the best order in which to hop between training passages. We tackle these problems via Baleen, a system that improves the accuracy of multi-hop retrieval while learning robustly from weak training signals in the many-hop setting. To tame the search space, we propose condensed retrieval, a pipeline that summarizes the retrieved passages after each hop into a single compact context. To model complex queries, we introduce a focused late interaction retriever that allows different parts of the same query representation to match disparate relevant passages. Lastly, to infer the hopping dependencies among unordered training passages, we devise latent hop ordering, a weak-supervision strategy in which the trained retriever itself selects the sequence of hops. We evaluate Baleen on retrieval for two-hop question answering and many-hop claim verification, establishing state-of-the-art performance.
Inside the Factory: Observing Musical Production

Here, I approach the core interests of this book from yet another angle. Having analyzed the sampling processes of released tracks, I here focus on a direct observation of a musical production in the making. This will offer further insights into the creative choices of a particular sampling process and allow a well-grounded discussion of sampling as a multilevel process, as I have defined the term before.
The Spider of Sampling Reasons (SSR)

Chapter 5 will introduce and develop an analytical tool: the spider of sampling reasons (SSR). This tool is based on the preceding discussion of the literature alongside data from my own anthropological research. It aims to facilitate the analysis of sample-based music, and will be applied in the case studies which follow. Chapter 5 also addresses, on a broader level, the question of why artists sample in experimental electronica.
Signature Recognition for Banking System

Abstract: The use of signatures for personal identification and verification is very common. Verifying signatures is necessary for many documents, including legal transactions and bank checks. The authentication of several papers using signatures is an extremely challenging and time-consuming job. As a result, systems for biometric personal verification and identification that rely on measurable, distinctive physical traits (such as fingerprints, hand geometry, faces, iris scans, or DNA) or behavioural traits have experienced an accelerating expansion (gait, voice etc.). Since tokens, passwords, pins, and other commonly used identity verification techniques have a number of fatal flaws and can't meet security requirements, the research looks into a feature that is far more often used: signature verification. We discuss the methods for authenticating signatures. We classify and outline the many techniques put forth for verification of signatures
Detecting Abnormal Activities in ATM Centres

Abstract: A given user enters the ATM, conducts the various tasks that are available, such as withdrawing cash, checking the balance in their bank account, and many other things. This is how an ATM of a particular bank operates in today's world, and it is not that difficult. In this study, we concentrate on what is overlooked amidst this normalised situation. A user may enter the ATM with his or her face partially or completely covered, making it impossible for the security camera that might be installed in that space to identify that person's facial features. In addition, there may be unlawful acts going on in the room without anyone taking urgent action to stop them or regulate them. Some users might bring illegal firearms into the ATM room, which might not even be seen by the installed CCTV camera. In this study, we want to enhance the facial recognition of ATM users such that their faces can be recognised even when partially or completely hidden. Then, if a certain user enters the room carrying an illegal weapon, a trigger should be sent to the neighbourhood police station. This is part of our effort to improve weapon detection