Reviewerly/exHarmony huggingface dataset:
On the Difficulty of Translating Free-Order Case-Marking Languages

Abstract Identifying factors that make certain languages harder to model than others is essential to reach language equality in future Natural Language Processing technologies. Free-order case-marking languages, such as Russian, Latin, or Tamil, have proved more challenging than fixed-order languages for the tasks of syntactic parsing and subject-verb agreement prediction. In this work, we investigate whether this class of languages is also more difficult to translate by state-of-the-art Neural Machine Translation (NMT) models. Using a variety of synthetic languages and a newly introduced translation challenge set, we find that word order flexibility in the source language only leads to a very small loss of NMT quality, even though the core verb arguments become impossible to disambiguate in sentences without semantic cues. The latter issue is indeed solved by the addition of case marking. However, in medium- and low-resource settings, the overall NMT quality of fixed-order languages remains unmatched.
An Analysis of Preservice Elementary Teacher Ability in Creative Writing Containing Character Education with Local Wisdom Persepective

Poetry can be an integrative media for character education in learning in elementary schools. However, the important role of poetry has not been matched by the results of writing poetry in elementary schools. This study aims to analyze the ability of preservice elementary teacher ability in poetry writing containing character education with local wisdom persepective. Mix method research approach is used with research design is triangulation design. The research subjects were 35 students of the elementary school teacher education program. The data were obtained through the technique of writing poetry tests and interviews. The analysis of the test used aspects of poetry writing skills. The research findings provide views that students have been able to write poetry with their own imagination, include the meaning of poetry, the advice, and are able to integrate character values with local wisdom. Mastery of the diction aspect is the lowest among other aspects that need to be improved. Recommendations of this research to educators and future research as a review for developing effective Indonesian language learning in the elementary school teacher education program, especially in productive language skills, namely creative writing with character education and local wisdom.
Reducing information asymmetry and building trust in projects using blockchain technology

The success of every construction project is highly dependent on effective communication and trust between key project participants. It is assumed that all participants in the project will work smoothly together to complete the project. Nevertheless, according to the principal-agent theory, there is information asymmetry between project participants because they also have their own self-interests. Information asymmetry is the situation in which one of the two parties is better informed than the other. Due to complexity of construction projects and the number of participants involved, implementation of blockchain technology would reduce information asymmetry. This paper provides a framework for implementation of blockchain technology in construction projects so as to reduce information asymmetry and enhance trust between project participants. Blockchain technology ensures that all project participants have access to all the information exchanged between them over the duration of the project, and thus helps in developing a more trustful relationship between them.
Application of Deep Learning-Based Integrated Trial-Error + Science, Technology, Reading/Writing, Engineer, Arts, Mathematics Teaching Mode in College Entrepreneurship Education

The purpose is to achieve a high-quality teaching effect in quality education using the new teaching concept. Firstly, the Deep Learning (DL) theory is introduced to improve the trial-error teaching method, then the trial-error teaching is combined with STREAM education. Afterward, the conical section hyperbola teaching in college entrepreneurship education is specifically studied under experimental analysis using the proposed DL-based integrated trail-error + STREAM teaching methods. The experimental results read: student’s average veracity on the multiple-choice question is 93.4% and 90.1% for the experimental group and control group, respectively; student’s average veracity on short-answer questions is 92.3% and 90.3% for the experimental group and control group, respectively. The results show that the application of DL to the trial-error teaching method can cultivate students’ in-depth analysis and logical thinking ability for mathematical problems. Meanwhile, the DL-based integrated trial-error + STREAM teaching methods stimulate students’ initiative to learn more difficult knowledge, establish integral knowledge systems, and more comprehensively and deeply understand the teaching content. As a result, students’ scientific literacy and humanistic literacy are both improved. Therefore, the proposed DL-based integrated trial-error + STREAM teaching method in college entrepreneurship education has a guiding significance for other disciplines and provides ideas for the expansion and development of STREAM teaching in the future.
Impact of AI technologies on organisational learning: proposing an organisation cognition schema

Purpose The work depicts an organizational learning schema, which reflects on the cognition structure of workplaces during the integration of AI-powered technologies. Design/methodology/approach This paper summarizes the most recent advances on organizational cognition from organizational studies. The article tries to capture how learning is impacted at the individual level and organization level, with incorporation of AI technologies. Findings The theoretical schema on organizational cognition could be studied and integrated to fill this gap and support an effective and smooth transition from electronic, computer-based industries (characteristics of the third industrial revolution) to AI powered enterprises. Originality/value It's one of the first works to discuss organization learning along with AI technologies.
Falcon Optimization Algorithm for Bayesian Networks Structure Learning

In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values.
Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification.

Breast cancer continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management. All breast lesions are not malignant, and all the benign lesions do not progress to cancer. However, the accuracy of diagnosis can be increased by a combination or preoperative tests such as physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy. Despite some limitations, these procedures are more accurate, reliable, and acceptable, when compared with a single adopted diagnostic procedure. Recent studies have shown that breast cancer can be accurately predicted and diagnosed using machine learning (ML) technology. The objective of this study was to explore the application of ML approaches to classify breast cancer based on feature values generated from a digitized image of a fine-needle aspiration (FNA) of a breast mass. To achieve this objective, we used ML algorithms, collected a scientific dataset of 569 breast cancer patients from Kaggle (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data), analyze and interpreted the data based on ten real-valued features of a breast mass FNA including the radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Among the 569 patients tested, 63% were diagnosed with benign breast cancer and 37% were diagnosed with malignant breast cancer. Benign tumors grow slowly and do not spread while malignant tumors grow rapidly and spread to other parts of the body.
Masking Feedforward Neural Networks Against Power Analysis Attacks

Abstract Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking , a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets , i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.
On intersection density of transitive groups of degree a product of two odd primes

Two elements $g$ and $h$ of a permutation group $G$ acting on a set $V$ are said to be intersecting if $g(v) = h(v)$ for some $v \in V$. More generally, a subset ${\cal F}$ of $G$ is an intersecting set if every pair of elements of ${\cal F}$ is intersecting. The intersection density $\rho(G)$ of a transitive permutation group $G$ is the maximum value of the quotient $|{\cal F}|/|G_v|$ where $G_v$ is a stabilizer of $v\in V$ and ${\cal F}$ runs over all intersecting sets in $G$. Intersection densities of transitive groups of degree $pq$, where $p>q$ are odd primes, is considered. In particular, the conjecture that the intersection density of every such group is equal to $1$ (posed in [ J.~Combin. Theory, Ser. A 180 (2021), 105390]) is disproved by constructing a family of imprimitive permutation groups of degree $pq$ (with blocks of size $q$), where $p=(q^k-1)/(q-1)$, whose intersection density is equal to $q$. The construction depends heavily on certain equidistant cyclic codes $[p,k]_q$ over the field $\mathbb{F}_q$ whose codewords have Hamming weight strictly smaller than $p$.
A Pragmatic Theory of Occupants' Indoor-Environmental Control Behaviour

Computational tools for building design and operation support entail fairly detailed representations of buildings' geometry, construction, and systems. Recent efforts aim at enhancing, in these tools, the relatively less developed models of building users. Thereby, one of the key challenges concerns the fit between the nature and level of needed support (e.g., performance queries) on the one hand and the required or appropriate resolution of the applied occupant model on the other hand. Some queries involving aggregate performance indicator may be sufficiently served by simple models of occupants' presence and actions in buildings. Detailed queries, however, may necessitate the implementation of high-resolution dynamic occupant representations. Methods to generate an occupant model may be fully data-driven, or they may be based on explicitly stated causal theories of human behaviour. However, there is not necessarily a sharp boundary between these approaches: Regularities harnessed by data-driven methods often reveal an implicit theoretical feature, as they are key to mapping processes from independent variables (model input) to dependent variables (manifest behaviour). Causal methods, on the other hand, need data to both develop and calibrate occupant methods. In this context, the present paper introduces the outline and main elements of a pragmatic theory of control-oriented human behaviour in buildings. The theory is suggested to inform the efforts towards construction of occupant models in computational applications related to building design, operation, and evaluation. Specifically, it can systematically guide the formulation of occupant-related ontologies and their instantiation in computational applications.
Cross‐Encoded Quantum Key Distribution Exploiting Time‐Bin and Polarization States with Qubit‐Based Synchronization

Abstract Robust implementation of quantum key distribution requires precise state generation and measurements, as well as the choice of an optimal encoding to minimize channel disturbance. Time‐bin encoding represent a good candidate for fiber links as birefingence does not perturb this kind of states whereas stable and low‐error encoders and decoders are available for polarization encoding. Here a cross‐encoded scheme where high accuracy quantum states are prepared through a self‐compensating, calibration‐free polarization modulator and transmitted using a polarization‐to‐time‐bin converter is presented. The receiver performs time‐of‐arrival measurements in the key‐generation basis and converts qubits back to polarization encoding for measurements in the control basis. Temporal synchronization between the transmitter and receiver is performed with a qubit‐based method that does not require additional hardware to share a clock reference. The system is tested in a 12‐h run and demonstrates good and stable performance in terms of key and quantum bit error rates. The flexibility of this approach represents an important step toward the development of hybrid networks with both fiber‐optic and free‐space links.
A Novel Password Policy Focusing on Altering User Password Selection Habits: A Statistical Analysis on Breached Data

Online services generally employ password-based systems to enable users to access personal/private content. These services also force their users to change their passwords periodically under specific policies to increase security. However, analysis of breached data reveals that current policies do not consider user password selection habits and pose critical security and privacy concerns. Additionally, when passwords are leaked, attackers have the opportunity to study - and possibly identify - the structure or pattern of the user password selection set. This way, attackers could predict the next password or reduce the search space considerably in their attacks. Therefore, this study proposes a novel behavior-based password policy to increase the present security level and avoid further exploitations if a breach occurs. This study uses statistical methods and visualization techniques to examine the password selection behaviors of over ten million UserID-password pairs collected from anonymously shared data breaches. The data set is anonymized while keeping the uniqueness of userID-password pairs and shared with other researchers along with extracted features. Results show that user password selection patterns can be generalized and used to increase the success rate of attacks.
The Cloud-Enabled Architecture of the Clinical Data Repository in Poland

In this article, the authors, using information-systems modeling techniques, and considering current national legal regulations, present the cloud-enabled architecture of a clinical data repository. The patient’s medical record is an important carrier of information necessary for accurate diagnosis and selection of the correct treatment process. Therefore, it is not surprising that since the beginning of the development of computer technologies, databases have been built to enable the management of a patient’s medical records. These systems were most-often deployed locally at individual healthcare units, which carried certain limitations both in terms of the security and availability of the stored information, and the possibility of exchanging it with other clinics. However, recent developments in the standardization of medical information exchange in Poland, together with the revolution in cloud computing, have opened up completely new perspectives for clinical-data-repository implementations helping to make them far more sustainable. Although, the practical aspects of implementing clinical-documentation repositories are studied both in forums of European countries and also around the world; so far, no similar research was conducted with respect to Poland. This study tries to fill that gap by proposing a flexible multi-variant cloud-enabled architecture of the system providing the services of a clinical-data repository. The goal of the work was to propose such a system architecture that allows having a system that is either cloud-agnostic, that uses specifically selected cloud services, or that is even deployable locally. Thanks to the use of cloud computing services, the implemented system is characterized by high availability, scalability, and the possibility of exchanging data between medical institutions, which enables the improvement in the quality of medical processes for the whole Polish population.
A Method for Class-Imbalance Learning in Android Malware Detection

More and more Android application developers are adopting many different methods against reverse engineering, such as adding a shell, resulting in certain features that cannot be obtained through decompilation, which causes a serious sample imbalance in Android malware detection based on machine learning. Hence, the researchers have focused on how to solve class-imbalance to improve the performance of Android malware detection. However, the disadvantages of the existing class-imbalance learning are mainly the loss of valuable samples and the computational cost. In this paper, we propose a method of Class-Imbalance Learning (CIL), which first selects representative features, uses the clustering K-Means algorithm and under-sampling to retain the important samples of the majority class while reducing the number of samples of the majority class. After that, we use the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm to generate minority class samples for data balance, and finally use the Random Forest (RF) algorithm to build a malware detection model. The result of experiments indicates that CIL effectively improves the performance of Android malware detection based on machine learning, especially for class imbalance. Compared with existing class-imbalance learning methods, CIL is also effective for the Machine Learning Repository from the University of California, Irvine (UCI) and has better performance in some data sets.
Blockchain and Cloud to Overcome the Problems of Buyer and Seller Watermarking Protocols

Copyright protection of digital content has become a problem not only for web content providers but also for ordinary web users who like to publish their digital contents on social or user generated content platforms. Among the possible solutions to such a problem, digital watermarking, in conjunction with watermarking protocols, appears to be a valid alternative to current DRM (digital rights management) systems. In fact, watermarking based solutions insert perceptually invisible copyright information into the copies of contents published or distributed on the web in order to track them. Such insertions are carried out according to the watermarking protocols, which have evolved over the years from the classic “buyer and seller” paradigm into a simpler and versatile “buyer friendly” and “mediated” approach. However, such an approach cannot exploit the new technologies that characterize the current Internet. This paper presents a new watermarking protocol able to adapt the “buyer friendly” and “mediated” approach to the use of innovative technologies such as cloud platforms and blockchain. In this way, (1) content providers and common web users can take advantage of the computing and storage resources made available by cloud platforms; (2) the involvement of trusted third parties in the protocols can be reduced by using blockchain without complicating the protection scheme. In fact, these two goals make the protocol particularly suited for the current Internet.
Nearly Exact Discrepancy Principle for Low-Count Poisson Image Restoration

The effectiveness of variational methods for restoring images corrupted by Poisson noise strongly depends on the suitable selection of the regularization parameter balancing the effect of the regulation term(s) and the generalized Kullback-Liebler divergence data term. One of the approaches still commonly used today for choosing the parameter is the discrepancy principle proposed by Zanella et al. in a seminal work. It relies on imposing a value of the data term approximately equal to its expected value and works well for mid- and high-count Poisson noise corruptions. However, the series truncation approximation used in the theoretical derivation of the expected value leads to poor performance for low-count Poisson noise. In this paper, we highlight the theoretical limits of the approach and then propose a nearly exact version of it based on Monte Carlo simulation and weighted least-square fitting. Several numerical experiments are presented, proving beyond doubt that in the low-count Poisson regime, the proposed modified, nearly exact discrepancy principle performs far better than the original, approximated one by Zanella et al., whereas it works similarly or slightly better in the mid- and high-count regimes.
t-BMPNet: Trainable Bitwise Multilayer Perceptron Neural Network over Fully Homomorphic Encryption Scheme

Homomorphic encryption (HE) is notable for enabling computation on encrypted data as well as guaranteeing high-level security based on the hardness of the lattice problem. In this sense, the advantage of HE has facilitated research that can perform data analysis in an encrypted state as a purpose of achieving security and privacy for both clients and the cloud. However, much of the literature is centered around building a network that only provides an encrypted prediction result rather than constructing a system that can learn from the encrypted data to provide more accurate answers for the clients. Moreover, their research uses simple polynomial approximations to design an activation function causing a possibly significant error in prediction results. Conversely, our approach is more fundamental; we present t-BMPNet which is a neural network over fully homomorphic encryption scheme that is built upon primitive gates and fundamental bitwise homomorphic operations. Thus, our model can tackle the nonlinearity problem of approximating the activation function in a more sophisticated way. Moreover, we show that our t-BMPNet can perform training—backpropagation and feedforward algorithms—in the encrypted domain, unlike other literature. Last, we apply our approach to a small dataset to demonstrate the feasibility of our model.