Reviewerly/exHarmony huggingface dataset:
Testing Connectedness of Images

We investigate algorithms for testing whether an image is connected. Given a proximity parameter $\epsilon\in(0,1)$ and query access to a black-and-white image represented by an $n\times n$ matrix of Boolean pixel values, a (1-sided error) connectedness tester accepts if the image is connected and rejects with probability at least 2/3 if the image is $\epsilon$-far from connected. We show that connectedness can be tested nonadaptively with $O(\frac 1{\epsilon^2})$ queries and adaptively with $O(\frac{1}{\epsilon^{3/2}} \sqrt{\log\frac{1}{\epsilon}})$ queries. The best connectedness tester to date, by Berman, Raskhodnikova, and Yaroslavtsev (STOC 2014) had query complexity $O(\frac 1{\epsilon^2}\log \frac 1{\epsilon})$ and was adaptive. We also prove that every nonadaptive, 1-sided error tester for connectedness must make $\Omega(\frac 1\epsilon\log \frac 1\epsilon)$ queries.
Open Access Transformation in Switzerland &amp;#038; Germany

Christian Gutknecht published an exciting posting on the Swiss EUR 57 million Elsevier deal in which he outlines the transformative Open Access agreement between Elsevier and swissuniversities. Since Germany has been trying for years to reach such a contract with Elsevier, it is worth comparing it with the two transformative contracts with Wiley and Springer Nature in Germany, which were reached and coordinated by Project DEAL.
Energy-Aware HDR Content End-to-End VVC Encoding

The proposed demonstration combines several commercially viable state-of-the-art technologies to achieve significant energy reduction while delivering HDR video without compromising QoE. The components of the demo are: (i) The demo is based our solution on VVC, the most efficient, highest quality video codec available today. (ii) We leverage Advanced HDR by Technicolor, a solution that uniquely can deliver HDR and SDR video using a single stream. (iii) We introduce a solution for dynamically adjusting peak brightness of displays via display adaptation controlled through the network using dynamic metadata encapsulated in MPEG SEI messages. This solution has been tested and shows a capability of reducing power by 10% or more without any subjective quality loss, and far more with minimal subjective impact. (iv) While we demonstrate a streaming (unicast) scenario in our live demo, our system is also capable of working in a dynamic unicast/multicast environment based on DVB transmission standards. (v) The entire demonstration is resulting from an industrial collaboration and is based on ready to market technologies and prototypes.
IMPLEMENTATION OF THE PRINCIPLE OF INTERDISCIPLINARITY IN THE STUDY OF AN INDUSTRIAL ROBOT

This article presents the results of studying the involvement of students in the learning process using the information and communication environment. The assessment of the degree of digital agency of students is given. The features of the use of an immersive educational environment for obtaining practical skills in general technical disciplines are analyzed.
ReseachGate is granted a patent for (semantic) citation analysis

The Library Journal two days ago informed about an interesting patent granted to the Social Network ResearchGate: “ResearchGate Granted U.S. Patent 10,282,424: ‘Linking Documents Using Citations’“. The abstract describes the patent as follows…”Aspects of the present disclosure relate to linking documents using citations. A server accesses a stored document in a data repository.
Impact of Research and Development Expenditures on Firm Level Innovation: Evidence from Developing Nations

Developing countries are lagging behind in terms of research and development expenditure and innovation activities. The ability to innovate depends upon the worker's knowledge, skills, training, and experience. It is widely believed that innovation drives growth and plays a vital role in firm survival. The study's objective was to investigate the effect of a firm's research and development expenditures on innovation activities in less developing nations using World Enterprise Survey data. The study utilized the multilevel logistic regression model. The innovation is measured through the survey question, “During the last three years, has this establishment introduced new or significantly improved products or services”. The results of the logistic regression model indicate that research and development expenditures has a positive impact on innovation. The all of other control variables such as registered firm, firm size, firm age and skilled worker also positively affected by research and development expenditures.
Mediating Role of Organizational Commitment between Perceived Organizational Support and Employee Perception in Public Sector Universities

This research aims to examine the mediating role of organizational commitment between perceived organizational support and employees' perceptions of unethical pro-organizational behavior in public sector universities. The sample of 233 individuals from public sector universities in Sindh and Baluchistan was collected using non-probability purposive sampling. Data was collected using an adapted five-Likert scale questionnaire. The primary technique for data analysis was PLS-SEM using SMART-PLS 4.0. The findings demonstrated the critical role of organizational commitment in public sector institutions and how it acts as a bridge between perceived corporate support and employee perception. However, job security and organizational trust's moderating effects in the study were not substantiated. Public universities also face particular difficulties in creating an organizational commitment culture, which mediates employee perception and perceived support. Employees obey management's directives and stick to established procedures, which promotes growth and generates creative ideas. This study concludes that organizational commitment is crucial in public sector universities in mediating the relationship between perceived support and employee perception. It helps in growth constructively and fosters creativity. Perceived organizational support contributes to building commitment, and providing a supportive work environment, above all, it boosts employee well-being. The research's findings will be useful in policy and decision-making processes in public sector institutions.
Update on the scidecode study on Persistent Identifiers (PIDs)

Here is an update on a study we are conducting at scidecode science consulting.
Contrastive representation learning in recommendation systems--The investigation of the performance of the self-supervised learning in large-scale recommendation systems

Self-supervised learning (SSL) has been proposed in machine learning projects for its convenience of reducing self-labeled datasets in recent years. However, the implementation of SSL in large-scale recommendation systems has lagged behind the evolution because of their scarce and tailed characteristics. In 2021, an article proposed the use of SSL in recommendation systems to pursue an improvement in the performance of recommender models. This article is built on this previous investigation and aims to further explore the role of SSL in recommendation systems and to investigate an improvement of the models efficiency. To answer research questions, this paper tests three models with different numbers of towers to discover the best performance of the use of SSL in recommender models. Consequently, it is found that implementing SSL on the item side only (two-tower DNNs) produced the best result. Then, when constructing the two-tower DNNs model, this article examines different numbers of negative pairs to change the InfoNCE loss to investigate a tradeoff between the number of positive and negative samples in the performance of the model. As a result, it turns out to be a weak correlation between this ratio and the performance; Hence, it is concluded that the change of the number of positive and negative samples would not necessarily affect the two-tower DNNs model. In our experiential stage, this paper uses a real-world dataset with 100k training samples to testify and compare our results.
Improving sentiment classification using a RoBERTa-based hybrid model

Introduction Several attempts have been made to enhance text-based sentiment analysis’s performance. The classifiers and word embedding models have been among the most prominent attempts. This work aims to develop a hybrid deep learning approach that combines the advantages of transformer models and sequence models with the elimination of sequence models’ shortcomings. Methods In this paper, we present a hybrid model based on the transformer model and deep learning models to enhance sentiment classification process. Robustly optimized BERT (RoBERTa) was selected for the representative vectors of the input sentences and the Long Short-Term Memory (LSTM) model in conjunction with the Convolutional Neural Networks (CNN) model was used to improve the suggested model’s ability to comprehend the semantics and context of each input sentence. We tested the proposed model with two datasets with different topics. The first dataset is a Twitter review of US airlines and the second is the IMDb movie reviews dataset. We propose using word embeddings in conjunction with the SMOTE technique to overcome the challenge of imbalanced classes of the Twitter dataset. Results With an accuracy of 96.28% on the IMDb reviews dataset and 94.2% on the Twitter reviews dataset, the hybrid model that has been suggested outperforms the standard methods. Discussion It is clear from these results that the proposed hybrid RoBERTa–(CNN+ LSTM) method is an effective model in sentiment classification.
Cognitive potential of agglutinative languages in intelligent technologies

This article describes an approach to development of new intelligent technologies based on the study of cognitive potential and semiotic modeling of lexical and grammatical structures of agglutinative languages (using the Tatar language as an example). A classification of cognitive lexical grammatical structures that determine the information technological effectiveness of a language is proposed. Specific examples show the relevant capabilities of the Tatar language for compact representation of knowledge, operating with fuzzy information, revealing such important properties as regularity, natural and constructive complexity, recursion, deep morphological ellipsis, functional diversity and semantic multivalence of affixes. The proposed approach, based on semiotic models of agglutinative language and the use of technological means of verbalization and contextual recognition of language meanings, can provide effective solutions for creating intelligent tools for accumulating and processing knowledge. Work in this direction creates the prospect of building a cognitive-communicative artificial intelligence system based on a universal language of communication between artificial intelligence systems as well as with humans.
The Two Sheriffs Problem: Cryptographic Formalization and Generalization

The two sheriffs problem is the following problem. There are two sheriffs, and each of them has their own list of suspects. Assuming that these lists are the result of a proper investigation, we can say that a culprit is the intersection of them even if the sheriffs do not know who the culprit is. Now, they wish to identify the culprit through an open channel, i.e., to compute the intersection of two lists, without letting an eavesdropper know the culprit who observed all communications. This cryptographic problem was proposed by Beaver et al., and a combinatorial solution using a bipartite graph was proposed. In this paper, we propose a formulation of the two sheriffs problem by introducing a secrecy evaluation based on the eavesdropper’s attack success probability. Furthermore, we propose an improved version of Beaver et al.’s protocol that an arbitrary number of players can execute and has less attack success probability.
Academic Achievement of Foreign Language Undergraduate Students during Pandemic Times

This research article aims to show the academic achievement in the form of marks of students of the third year’s subject ‘Teaching and Learning English’ located in the Degree in Primary Education (Faculty of Education, Universidad de Murcia, Spain). Our corpus of participants has five different groups (including the bilingual group) with a total number of n=1496 students with an average per academic year of n=374 students whose marks have been analysed comparing four different academic years. Taking into account the subject programme, the performance of the students during the normal year, during the lockdown, in the blended learning yeart (face-to-face and online) and in the return-to-normal year have been analysed. The results show that in both the lockdown and the blended learning year the performance is higher than in the other academic years, which leads us to question whether the pandemic really influenced the assessment and teaching conditions of the subject.
Detection and Hardening Strategies to Secure an Enterprise Network

In today’s IT enterprises, security strategy determination has become exponentially complex with the increasing complexity of the network infrastructure. Various types of defenses are available with a security administrator, viz., harden, detect, isolate, deceive, and evict. These defenses have their specific purposes. Separate strategies are required for implementing each type of defense in the context of an enterprise network. The existing defense strategy selection schemes do not have explicit strategies for different classes of defenses. In this paper, we propose two separate strategies to determine the point of deployment of harden and detect defenses. These strategies would be useful in providing a better return on security investments.
Springer Nature reaches £9.6 million agreement in the UK &amp;#8211; not included: Nature-branded titles

According to a press release by Springer Nature there is a new contract of the group with Joint Information Systems Committee (JISC). The deal is based on the principle of “read and publish” and shall fulfill the requirements of Plan S. As noted by Springer Nature the “agreement limits the costs of publishing all UK articles open access (OA) while maintaining access to all of Springer’s subscription articles.
APLIKASI PEMBELAJARAN SYNCHRONOUS DAN ASYNCHRONOUS DI PERGURUAN TINGGI

Abstrak: Kemajuan teknologi saat ini telah menggerakkan dosen dan mahasiswa untuk menggunakan media pembelajaran berbasis teknologi informasi (TI) yang mendukung kegiatan perkuliahan. Penelitian ini melihat bagaimana penerapan aplikasi pembelajaran synchronous dan asynchronous di Fakultas Tarbiyah dan Ilmu Keguruan IAIN Lhokseumawe dan bagaimana perspektif mahasiswa terhadap penggunaan aplikasi pembelajaran synchronous dan asynchronous tersebut. Salah satu cara pembelajaran jarak jauh adalah pembelajaran Synchronous dan Asynchronous. Penelitian ini menggunakan pendekatan kualitatif deskriptif. Hasil penelitian ditemukan jika dosen yang mengajar di Fakultas Tarbiyah dan Ilmu Keguruan IAIN Lhokseumawe ditemukan jika pada sistem pembelajaran synchronous, dosen banyak menggunakan aplikasi pembelajaran synchronous yaitu Zoom Meeting, WhatsApp, Google Meet atau Google Classroom dan aplikasi pembelajaran asynchronous yaitu video youtube, power point, e-book, canva, quizziz, kahoot. Sebanyak 89% mahasiswa tertarik dan merasa efektif menggunakan aplikasi pembelajaran synchronous dan 92% mahasiswa tertarik, merasa efektif dan termotivasi dengan penggunaan aplikasi pembelajaran asynchronous. Kata Kunci: aplikasi pembelajaran, synchronous, asynchronous. Abstract: Current technological advances have motivated faculty and students to use information technology-based learning media to support the teaching activities. This study looks at how the application of synchronous and asynchrone learning applications in the Faculty of education and teacher training IAIN Lhokseumawe and how the student's perspective towards the use of such synchronic and asymphonic learning applications. One way of distance learning is synchronous and asynchrone learning. This research uses a descriptive qualitative approach. The results of the research were found if the lecturer who teaches at the Fakultas Tarbiyah dan Ilmu Keguruan IAIN Lhokseumawe found that on the synchronous learning system, the teacher used a lot of synchronized learning applications such as Zoom Meeting, WhatsApp, Google Meet or Google Classroom and asynchrone learning apps such as video youtube, power point, e-book, canva, quizziz, kahoot. 89% of students were interested and felt effective using synchronous learning applications and 92% were interested, felt effective and motivated by using asynchrone learning applications. Keywords: learning application, synchronous, asynchronou
Privacy-preserving quantum federated learning via gradient hiding

Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes. Meanwhile, the critical concern of privacy within distributed computing protocols remains a significant challenge, particularly in standard classical federated learning (FL) scenarios where data of participating clients is susceptible to leakage via gradient inversion attacks by the server. This paper presents innovative quantum protocols with quantum communication designed to address the FL problem, strengthen privacy measures, and optimize communication efficiency. In contrast to previous works that leverage expressive variational quantum circuits or differential privacy techniques, we consider gradient information concealment using quantum states and propose two distinct FL protocols, one based on private inner-product estimation and the other on incremental learning. These protocols offer substantial advancements in privacy preservation with low communication resources, forging a path toward efficient quantum communication-assisted FL protocols and contributing to the development of secure distributed quantum machine learning, thus addressing critical privacy concerns in the quantum computing era.
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use

In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models (LLMs) significantly affects their performance in tasks demanding a high degree of context awareness, such as utilizing LLMs for tool-use. Specifically, the crucial information in the context will be potentially overlooked by model when it is positioned in the trough zone of the attention waveform, leading to decreased performance. To address this issue, we propose a novel inference method named Attention Buckets. It allows LLMs to process their input through multiple parallel processes. Each process utilizes a distinct base angle for the rotary position embedding, thereby creating a unique attention waveform. By compensating an attention trough of a particular process with an attention peak of another process, our approach enhances LLM's awareness to various contextual positions, thus mitigating the risk of overlooking crucial information. In the largest tool-use benchmark, our method elevates a 7B model to achieve state-of-the-art performance, comparable to that of GPT-4. On other benchmarks and some RAG tasks, which also demand a thorough understanding of contextual content, Attention Buckets also exhibited notable enhancements in performance.
GenDeF: Learning Generative Deformation Field for Video Generation

We offer a new perspective on approaching the task of video generation. Instead of directly synthesizing a sequence of frames, we propose to render a video by warping one static image with a generative deformation field (GenDeF). Such a pipeline enjoys three appealing advantages. First, we can sufficiently reuse a well-trained image generator to synthesize the static image (also called canonical image), alleviating the difficulty in producing a video and thereby resulting in better visual quality. Second, we can easily convert a deformation field to optical flows, making it possible to apply explicit structural regularizations for motion modeling, leading to temporally consistent results. Third, the disentanglement between content and motion allows users to process a synthesized video through processing its corresponding static image without any tuning, facilitating many applications like video editing, keypoint tracking, and video segmentation. Both qualitative and quantitative results on three common video generation benchmarks demonstrate the superiority of our GenDeF method.
Leveraging Transformer-based Language Models to Automate Requirements Satisfaction Assessment

Requirements Satisfaction Assessment (RSA) evaluates whether the set of design elements linked to a single requirement provide sufficient coverage of that requirement -- typically meaning that all concepts in the requirement are addressed by at least one of the design elements. RSA is an important software engineering activity for systems with any form of hierarchical decomposition -- especially safety or mission critical ones. In previous studies, researchers used basic Information Retrieval (IR) models to decompose requirements and design elements into chunks, and then evaluated the extent to which chunks of design elements covered all chunks in the requirement. However, results had low accuracy because many critical concepts that extend across the entirety of the sentence were not well represented when the sentence was parsed into independent chunks. In this paper we leverage recent advances in natural language processing to deliver significantly more accurate results. We propose two major architectures: Satisfaction BERT (Sat-BERT), and Dual-Satisfaction BERT (DSat-BERT), along with their multitask learning variants to improve satisfaction assessments. We perform RSA on five different datasets and compare results from our variants against the chunk-based legacy approach. All BERT-based models significantly outperformed the legacy baseline, and Sat-BERT delivered the best results returning an average improvement of 124.75% in Mean Average Precision.
Geometric phases in generalized radical Floquet dynamics

The Pancharatnam phase is a generalization of the Berry phase that applies to discrete sequences of quantum states. Here, we show that the Pancharatnam phase is a natural invariant for a wide class of quantum many-body dynamics involving measurements. We specifically investigate how a non-trivial Pancharatnam phase arises in the trajectories of Floquet quantum error-correcting codes and show that this phase can be extracted in a "computationally-assisted" interferometry protocol, involving additional post-processing based on the measurement record that defines a given quantum many-body trajectory. This Pancharatnam phase can also be directly related to the Berry phase accrued by continuous unitary evolution within a gapped phase. For the $\mathbb Z_2$ Floquet code of Hastings and Haah, we show that the associated family of unitary evolutions is the radical chiral Floquet phase. We demonstrate this correspondence explicitly by studying an exactly-solvable model of interacting spins.