Reviewerly/exHarmony huggingface dataset:
CAM-NAS: An Efficient and Interpretable Neural Architecture Search Model Based on Class Activation Mapping

Artificial intelligence (AI) has made rapid progress in recent years, but as the complexity of AI models and the need to deploy them on multiple platforms gradually increases, the design of network model structures for specific platforms becomes more difficult. A neural network architecture search (NAS) serves as a solution to help experts discover new network structures that are suitable for different tasks and platforms. However, traditional NAS algorithms often consume time and many computational resources, especially when dealing with complex tasks and large-scale models, and the search process can become exceptionally time-consuming and difficult to interpret. In this paper, we propose a class activation graph-based neural structure search method (CAM-NAS) to address these problems. Compared with traditional NAS algorithms, CAM-NAS does not require full training of submodels, which greatly improves the search efficiency. Meanwhile, CAM-NAS uses the class activation graph technique, which makes the searched models have better interpretability. In our experiments, we tested CAM-NAS on an NVIDIA RTX 3090 graphics card and showed that it can evaluate a submodel in only 0.08 seconds, which is much faster than traditional NAS methods. In this study, we experimentally evaluated CAM-NAS using the CIFAR-10 and CIFAR-100 datasets as benchmarks. The experimental results show that CAM-NAS achieves very good results. This not only proves the efficiency of CAM-NAS, but also demonstrates its powerful performance in image classification tasks.
Studies and design considerations for animated transitions between small-scale visualizations

Abstract Small-scale visualizations can augment text, show information on mobile devices, or geographical information on a map. In such situations, there is often not enough space to show complex data with approaches like juxtaposed visualizations. To alleviate this issue, we propose the use of animated transitions between several small-scale visualizations. We discuss design considerations for animated transitions between small-scale visualizations and differences to normal-sized visualizations. Further, we present the results of two online studies on the effectiveness of those animated transitions to convey information and attribute relations, as well as the mental load of following the animated transitions. As a result, we found that animated transitions between visualizations are understandable in small scale, but performance depends on the specific task and the type of operation carried out with the animated transition. Graphical abstract
Transparent Reduction of Dimension with Genetic Algorithm

There are domain areas where all transformations of data must be transparent and interpretable (medicine and finance for example). Dimension reduction is an important part of a preprocessing pipeline but algorithms for it are not transparent at the current time. In this work, we provide a genetic algorithm for transparent dimension reduction of numerical data. The algorithm constructs features in a form of expression trees based on a subset of numerical features from the source data and common arithmetical operations. It is designed to maximize quality in binary classification tasks and generate features explainable by a human which achieves by using human-interpretable operations in a feature construction. Also, data transformed by the algorithm can be used in a visual analysis. The multicriterial dynamic fitness function is provided to build features with high diversity.
Quantifying quantum causal influences

Causal influences are at the core of any empirical science, the reason why its quantification is of paramount relevance for the mathematical theory of causality and applications. Quantum correlations, however, challenge our notion of cause and effect, implying that tools and concepts developed over the years having in mind a classical world have to be reevaluated in the presence of quantum effects. Here, we propose the quantum version of the most common causality quantifier, the average causal effect, measuring how much a target quantum system is changed by interventions on its presumed cause. Not only does it offer an innate manner to quantify causation in two-qubit gates but also in alternative quantum computation models such as the measurement-based version, suggesting that causality can be used as a proxy for optimizing quantum algorithms. Considering quantum teleportation, we show that any pure entangled state offers an advantage in terms of causal effects as compared to separable states. This broadness of different uses showcases that, just as in the classical case, the quantification of causal influence has foundational and applied consequences and can lead to a yet totally unexplored tool for quantum information science.
Quantum channel decomposition with preselection and postselection

The quantum channel decomposition techniques, which contain the so-called probabilistic error cancellation and gate or wire cutting, are a powerful approach for simulating a hard-to-implement (or an ideal) unitary operation by concurrently executing relatively easy-to-implement (or noisy) quantum channels. However, such virtual simulation necessitates an exponentially large number of decompositions, thereby significantly limiting their practical applicability. This paper proposes a channel decomposition method for target unitaries that have their input and output conditioned on specific quantum states, namely, unitaries with pre- and postselection. Specifically, we explicitly determine the requisite number of decomposing channels, which could be significantly smaller than the selection-free scenario. Furthermore, we elucidate the structure of the resulting decomposed unitary. We demonstrate an application of this approach to the quantum linear solver algorithm, highlighting the efficacy of the proposed method.
FGSI: distant supervision for relation extraction method based on fine-grained semantic information

Relation extraction is one of the important steps in building a knowledge graph. Its main objective is to extract semantic relationships from identified entity pairs in sentences, playing a crucial role in semantic understanding and knowledge graph construction. Remote supervised relation extraction aligns knowledge bases with natural language texts and generates labeled data, which alleviates the burden of manually annotating datasets. However, the labeled corpus obtained from remote supervision contains a large amount of noisy data, which greatly affects the training of relation extraction models. In this paper, we propose the hypothesis that key semantic information within the sentence plays a crucial role in entity relation extraction in the task of remote supervised relation extraction. Based on this hypothesis, we divide the sentence into three segments by splitting it according to the positions of entities, starting from within the sentence. Then, using intra-sentence attention mechanisms, we identify fine-grained semantic features within the sentence to reduce the interference of irrelevant noise information. We also improved the intra-bag attention mechanism by setting a threshold gate to filter out low-relevant noisy sentences, minimizing the impact of noise on the relation extraction model, and making full use of available positive semantic information. Experimental results show that the proposed relation extraction model in this paper achieves improvements in precision-recall curve, P@N value, and AUC value compared to existing methods, demonstrating the effectiveness of this model.
Low-coherence optical profilometry with multi-reflection reference mirrors for large-depth object measurement

A new, to the best of our knowledge, optical configuration for digital holographic profilometry for surface profile measurement of large-depth objects is proposed. Two multi-reflection mirrors were employed to extend the maximum axial measurable range by a factor of 2 without any degradation of the spatial resolution. By adjusting the distance and the position of the two multi-reflection reference mirrors, the system can be made more flexible for measuring different parts of the object. In addition to the axial extension, the two-mirror system also increases the visibility of the interference fringes so that the object profile can be measured with high accuracy.
Representing dynamic textures based on polarized gradient features

Efficiently representing dynamic textures (DTs) is one of the significant challenges for video understanding in real implementations of computer vision applications. In this work, an efficient approach for DT description is introduced by addressing the following prominent concepts. Firstly, high-order 2D/3D Gaussian-gradient filtering kernels are used for filtering a given video to obtain its Gaussian-gradient-filtered images/volumes. Secondly, taking advantage of the polarizing property of these responses, we propose a competent model of decomposition to decompose them into corresponding robust collections of separately polarized outcomes, which are complementary to DT representation. Finally, a simple variant of local binary patterns (LBPs) is applied to extract local polarized Gaussian-gradient features from the complemented collections for constructing discriminative local-based descriptors. Experimental results for DT recognition on benchmark datasets have remarkably validated the efficacy of our proposal.
Novel Approach for Key-Based Hashing Algorithm

The existing key-based hashing algorithms, such as HMAC, NMAC and GMAC are highly dependent on pre-existing hashing algorithms such as MD5 and SHA1 which are self-sufficient and independent from the usage of keys. Hence, as a result, the key-based hashing algorithms inherit many aspects of security from its underlying algorithm and usually enhance it. But this feature also accounts for the loopholes in key-based hashing algorithms. The key-based hashing algorithms also inherit the number of rounds, used to hash the message, from its underlying algorithm as well. A fixed number of rounds also allows attacks on particular rounds in the algorithm to crack it. In this paper, a new, independent hashing algorithm is introduced which does not have a fixed number of rounds and is based on basic mathematical and Boolean concepts of Bitwise Operators. The performance and security analysis have shown in results, that the proposed algorithm is resistant towards known attacks on hashing algorithms and a few other attacks proposed by other researchers. Its performance analysis shows that it works in O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3/2</sup> ) time complexity which is efficient than O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) Time Complexity used in many widely used Key-Based Hashing Algorithms.
Universal Acceptance as an Elixir for Multilingual Internet and Cybersecurity Considerations

Universal Acceptance is fundamental for achieving a multilingual internet where users can browse the web in their preferred language or script. Universal Acceptance (UA) guarantees that all Internet-capable programs, hardware, and software handle all domain names and emails identically. UA is the entry point for the following billion Internet users. For decades the Internet has been working on a single language, i.e., English. Nevertheless, with the increasing demand for bringing back cultural language from every aspect, many websites and online services use multilingual pages for the feasibility of the user. A person who reads or studies anything in their native language is more likely to understand it quickly. Multilingual websites will help the individual navigate through it more convincingly than in any other language. Universal Acceptance will serve as the main concoction for making the idea of Multilingualism into reality through the Internet. This paper explores the need for Multilingualism on the global Internet, the eventual solution for bringing this transition to cyberspace via UA, and cybersecurity considerations.
Teachers’ digital competence in the post COVID-19 era: The effects of digital nativeness, and digital leadership capital

The COVID-19 pandemic, which posed challenges for accommodating student learning, also opened avenues for using digital resources in online learning. However, differences were observed in their use, effectiveness, and intensity across developed and least developed societies. This is referred to as “digital inequalities,” caused by factors such as insufficient organizational-level support and teachers’ digital competence (DC). This study was conducted to determine teachers’ DC and reveal if their DC was explained by their digital nativeness and principals’ digital instructional leadership capital. The study used a quantitative research method, whereby data were collected from 393 teachers. The researchers used SmartPLS 4 and SPSS 24 to analyze data. The findings complement the available literature and help pave ways to promote the integration of digital resources in teaching and learning.
Identifying barriers faced by university EFL instructors in integrating mobile-assisted language learning

The present study aimed to examine the barriers faced by Taiwanese university English as a foreign language (EFL) instructors when incorporating mobile-assisted language learning (MALL) into their teaching, and to explore how these barriers are influenced by the instructors’ background characteristics. To assess the variables, a survey questionnaire was developed and administered to a sample of 350 university instructors. Additionally, interviews were conducted with a subset of instructors to complement and clarify the quantitative data. The findings indicated that university EFL instructors encountered moderate barriers when integrating MALL into their teaching practices. Furthermore, the study identified significant differences in the barriers experienced based on the amount of time instructors dedicated to MALL on a daily basis. Both quantitative and qualitative data highlighted the importance of time as a critical factor for successful MALL integration. As a recommendation, it is advised that university administrators consider reducing instructors’ workload to facilitate the seamless integration of MALL.
Decoherence effects in the quantum Monty Hall solution using W state

The emergence of quantum computing has made us utilize its powers in solving the Monty Hall problem. The problem is depicted as a two-player game, where the two players, i.e. the host and the participant share an entangled three-qubit quantum state between them. The quantum state of three qubits is closely knit with the fact that behind which door would one expect the prize of the game to be. The two players are part of one team as they share a quantum communication channel amongst them by sharing the entangled state as a quantum resource. The article shows a unique way of utilizing the effectiveness and entanglement features of the W state as a source of communication in disguise for solving the Monty Hall problem. It is shown that by sharing the W state, the players always win the prize. Moreover, since Noisy Intermediate-Scale Quantum technology is the need of the hour, the effects of decoherence are also observed on the winning prospects of the game.
Investigating Relationship Conflict within the Social Network of Large IS Projects using a SIR Model

In large corporate organisations, significant business transformation initiatives are often implemented using large IS and IT projects that involve multiple third-party providers. These are often structured around multi-partner project teams that align to the various functional and technical aspects of the new system, incorporating a diversity of members within each project team, which frequently results in conflicts that spread across the social network of the implementation programme. While several studies on conflict in project teams have been published, to the best of our knowledge, there are no visual simulations that allow observers to examine the underlying social relationships and system dynamics. As a result, we believe exploring the process of conflict propagation through computational modelling will become a critical component of the study of conflict management. A common approach for modelling complex systems is agent-based modelling and simulation, which enables complex emergent behaviours to emerge at the system-level through simple rule-based interactions at the individual agent-level. We developed an agent-based model of conflict that incorporporated the Susceptible-Infectious-Removed model from epidemiology, in order investigate the propagation of conflict in a large IS implementation. Conflict is viewed as an infection, and the process of post-conflict management as patient recovery. We illustrate the model’s validity in reality and the results demonstrate that regardless of the magnitude of the conflict, if it is not adequately handled at the start, it may rapidly spread throughout the network.
NLP-Assisted Educational Memory Game Experiment

Memory card games are well-known for everyone. Their main goal is to find matching pairs among flipped cards by simultaneously turning two cards up. In each round information obtained and memorized from the previous round is used to predict matching pair positions. Whilst playing, a player’s thinking, memory, concentration, and attention skills can be improved, therefore, incorporating it into the learning process may be beneficial to the learner. In this research, we present a method for learning terminology from a scientific context by playing an AI-generated memory card game. We employ a general-purpose conversation chatbot, ChatGPT to generate the keyword-description pairs from a given scientific text. The primary purpose of our study is to evaluate the outcomes, with respect to their scientific accuracy and educational value. Consequently, we present a straightforward approach to constructing a game that facilitates the acquisition of knowledge by students in an enjoyable and playful way.
Subsumption-Linear Q-Resolution for QBF Theorem Proving

Subsumption-Linear Q-Resolution (SLQR) is introduced for proving theorems from Quantified Boolean Formulas. It is an adaptation of SL-Resolution, which applies to propositional and first-order logic. In turn SL-Resolution is closely related to model elimination and tableau methods. A major difference from QDPLL (DPLL adapted for QBF) is that QDPLL guesses variable assignments, while SLQR guesses clauses. In prenex QBF (PCNF, all quantifier operations are outermost) a propositional formula D is called a nontrivial consequence of a QBF $$\varPsi $$ if $$\varPsi $$ is true (has at least one model) and D is true in every model of $$\varPsi $$ . Due to quantifiers, one cannot simply negate D and look for a refutation, as in propositional and first-order logic. Most previous work has addressed only the case that D is the empty clause, which can never be a nontrivial consequence. This paper shows that SLQR with the operations of resolution on both existential and universal variables as well as universal reduction is inferentially complete for closed PCNF that are free of asymmetric tautologies; i.e., if D is logically implied by $$\varPsi $$ , there is a SLQR derivation of D from $$\varPsi $$ . A weaker form called SLQR–ures omits resolution on universal variables. It is shown that SLQR–ures is not inferentially complete, but is refutationally complete for closed PCNF.
Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

Abstract Maximum likelihood estimation (MLE) is an effective method for localizing radioactive sources in a given area. However, it requires an exhaustive search for parameter estimation, which is time-consuming. In this study, heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors. Hence, the time consumption of MLE would be effectively reduced. First, the radiation source was detected using the k -sigma method. Subsequently, the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source. A comparative study was performed in which the estimation accuracy and time consumption of the MLE were evaluated for traditional methods and heuristic techniques. The traditional MLE was performed via a grid search method using fixed and multiple resolutions. Additionally, four commonly used heuristic algorithms were applied: the firefly algorithm (FFA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC). The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program. The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE. The time consumption for the heuristic-based MLE was 0.75, 0.03, 0.02, and 0.059 s for FFA, PSO, ACO, and ABC, respectively. The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE. Hence, heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.
Treatment of Dug Well Water in Kubu Raya, West Kalimantan, with Electrocoagulation

Abstract Dug well water is one of the clean water sources used in Kubu Raya Regency. However, through the initial testing, the physical and chemical qualities of the water were inadequate to meet the hygiene and sanitation demands of daily living following Regulation No. 32 of the Minister of Health of Indonesia 2017. Consequently, the objective of this study was to examine the treatment of dug well water by electrocoagulation and electrocoagulation-coagulation employing Poly Aluminium Chloride (PAC) as a coagulant with varying periods (45, 60, 75, and 90 minutes). Increasing the processing time in the electrocoagulation treatment enhanced the pH and TDS values while lowering the color, turbidity, Fe, and Mn metals. The pH value increased for the electrocoagulation-coagulation treatment. Conversely, the concentrations of color, turbidity, Fe metal, and Mn metal dropped, however, the TDS value increased, exceeding the threshold. Ultimately, the electrocoagulation treatment was more effective at reducing contaminants, as measured by advances in efficiency for pH, turbidity, color, Fe, and Mn.