Reviewerly/exHarmony huggingface dataset:
Introduction and elimination, left and right

Functional programming language design has been shaped by the framework of natural deduction, in which language constructs are divided into introduction and elimination rules for producers of values. In sequent calculus-based languages, left introduction rules replace (right) elimination rules and provide a dedicated sublanguage for consumers of values. In this paper, we present and analyze a wider design space of programming languages which encompasses four kinds of rules: Introduction and elimination, both left and right. We analyze the influence of rule choice on program structure and argue that having all kinds of rules enriches a programmer’s modularity arsenal. In particular, we identify four ways of adhering to the principle that ”the structure of the program follows the structure of the data“ and show that they correspond to the four possible choices of rules. We also propose the principle of bi-expressibility to guide and validate the design of rules for a connective. Finally, we deepen the well-known dualities between different connectives by means of the proof/refutation duality.
Implementation of Photonic Phase Gate and Squeezed States via a Two-Level Atom and Bimodal Cavity

We propose a theoretical model for realizing a photonic two-qubit phase gate in cavity QED using a one-step process. The fidelity and probability of success of the conditional quantum phase gate is very high in the presence of cavity decay. Our scheme only employs one two-level atom, and thus is much simpler than other schemes involving multi-level atoms. This proposal can also be applied to generate two-mode squeezed states; therefore, we give three examples, i.e., the two-mode squeezed vacuum state, two-mode squeezed odd coherent state, and two-mode squeezed even coherent state, to estimate the variance of Duan’s criterion when taking into account cavity decay. It is shown that the variance is smaller than 2 for the three squeezed states in most cases. Furthermore, we utilize logarithmic negativity to measure the entanglement, and find that these squeezed states have very high degrees of entanglement.
Democracy, Corruption and Income: Evidence from Sub-Saharan Africa

The objective of this paper is to investigate the effect that democracy and corruption have on income in sub-Saharan Africa. The results show that whether or not democracy has income effects depends on a particular indicator used. While property rights and political stability have had positive income effects rule of law and government effectiveness have shown negative impact on income over the period under consideration. Corruption has negative impact on income. The study concludes that policy reforms should target programs that seek to develop and build the capacities of judiciaries, legislatures, media and civil societies to help enforce rule of law and strengthen democratic institutions. For research, the findings imply that researchers should seriously take into account the selection of proxies when investigating the impact of democracy on income.
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search

Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task. The WSI embeddings obtained from current methods are in Euclidean space not ideal for efficient WSI retrieval. Furthermore, most of the current methods require high GPU memory due to the simultaneous processing of multiple sets of patches. To address these challenges, we propose a novel framework for learning binary and sparse WSI representations utilizing a deep generative modelling and the Fisher Vector. We introduce new loss functions for learning sparse and binary permutation-invariant WSI representations that employ instance-based training achieving better memory efficiency. The learned WSI representations are validated on The Cancer Genomic Atlas (TCGA) and Liver-Kidney-Stomach (LKS) datasets. The proposed method outperforms Yottixel (a recent search engine for histopathology images) both in terms of retrieval accuracy and speed. Further, we achieve competitive performance against SOTA on the public benchmark LKS dataset for WSI classification.
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

Modern approaches for simulation-based inference rely upon deep learning surrogates to enable approximate inference with computer simulators. In practice, the estimated posteriors' computational faithfulness is, however, rarely guaranteed. For example, Hermans et al. (2021) show that current simulation-based inference algorithms can produce posteriors that are overconfident, hence risking false inferences. In this work, we introduce Balanced Neural Ratio Estimation (BNRE), a variation of the NRE algorithm designed to produce posterior approximations that tend to be more conservative, hence improving their reliability, while sharing the same Bayes optimal solution. We achieve this by enforcing a balancing condition that increases the quantified uncertainty in small simulation budget regimes while still converging to the exact posterior as the budget increases. We provide theoretical arguments showing that BNRE tends to produce posterior surrogates that are more conservative than NRE's. We evaluate BNRE on a wide variety of tasks and show that it produces conservative posterior surrogates on all tested benchmarks and simulation budgets. Finally, we emphasize that BNRE is straightforward to implement over NRE and does not introduce any computational overhead.
Naimark dilations of qubit POVMs and joint measurements

Measurement incompatibility is one of the cornerstones of quantum theory. This phenomenon appears in many forms, of which the concept of non-joint measurability has received considerable attention in the recent years. In order to characterise this non-classical phenomenon, various analytical and numerical methods have been developed. The analytical approaches have mostly concentrated on the qubit case, as well as to scenarios involving sets of measurements with symmetries, such as position and momentum or sets of mutually unbiased bases. The numerical methods can, in principle, decide any finite-dimensional and discrete joint measurability problem, but they naturally have practical limitations in terms of computational power. These methods exclusively start from a given set of measurements and ask whether the set possesses incompatibility. Here, we take a complementary approach by asking which measurements are compatible with a given measurement. It turns out, that this question can be answered in full generality through a minimal Naimark dilation of the given measurement: the set of interest is exactly those measurements that have a block-diagonal representation in such dilation. We demonstrate the use of the technique through various qubit examples, leading to an alternative characterisation of all compatible pairs of binary qubit measurements, which retrieves the celebrated Busch criterion. We further apply the technique to special examples of trinary and continuous qubit measurements.
Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the tree is built and the selection process plays a fundamental role in this. One particular selection mechanism that has proved to be reliable is based on the Upper Confidence Bounds for Trees (UCT). The UCT attempts to balance exploration and exploitation by considering the values stored in the statistical tree of the MCTS. However, some tuning of the MCTS UCT is necessary for this to work well. In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT formula and use the evolved expressions in MCTS. More specifically, we evolve expressions by means of our proposed Semantic-inspired Evolutionary Algorithm in MCTS approach (SIEA-MCTS). This is inspired by semantics in Genetic Programming (GP), where the use of fitness cases is seen as a requirement to be adopted in GP. Fitness cases are normally used to determine the fitness of individuals and can be used to compute the semantic similarity (or dissimilarity) of individuals. However, fitness cases are not available in MCTS. We extend this notion by using multiple reward values from MCTS that allow us to determine both the fitness of an individual and its semantics. By doing so, we show how SIEA-MCTS is able to successfully evolve mathematical expressions that yield better or competitive results compared to UCT without the need of tuning these evolved expressions. We compare the performance of the proposed SIEA-MCTS against MCTS algorithms, MCTS Rapid Action Value Estimation algorithms, three variants of the *-minimax family of algorithms, a random controller and two more EA approaches. We consistently show how SIEA-MCTS outperforms most of these intelligent controllers in the challenging game of Carcassonne.
Assessment of Factors that Affect Market Orientation of Cassava Farmers in Osun State, Nigeria

The study examined market orientation and market information sources for cassava farmers in Osun State, Nigeria. Multi-stage sampling procedure was used to select one hundred and twenty (120) respondents. With the use of structured questionnaire, data were collected on socioeconomic characteristics, sources of market information, specific market information available to cassava farmers, market orientation and entrepreneurial competence. Descriptive statistical tools (frequency counts, percentages, mean) and inferential statistical tool [Pearson’s Product Moment Correlation (PPMC)] were used for data analysis. Results revealed that majority (73.3%) were male with mean age of 46.48 years, and 73.3% had formal education. Most (62.5%) of the farmers had high market orientation and most sought information was on post-harvest technologies. However, the cassava farmers used less of marketing websites (17.5%), extension services (27.5%), email (29.2) and social media (35.8%) as sources of market information, and sought less of agricultural extension service information (34.2%). The study concluded that selected socio-economic characteristics and entrepreneurial competences were insignificant toward market orientation in cassava production. The study recommended that market orientation efforts should be geared toward cassava producers regardless of socio-economic status and entrepreneurial competence. Also, cassava farmers association should be willing to collaborate with extension services to build Market Information System that will be suitable to fill their specific needs at becoming market-oriented in cassava production.
Pemanfaatan Media Infografis Dalam Pembelajaran Sejarah di SMA Al-Khairiyah Samarinda

This study aims to determine the use of infographic media in learning history at SMA Al-Khairiyah Samarinda. The discussion in this study will focus on two things. First, to find out how to use infographic media in learning history at SMA Al-Khairiyah Samarinda. Second, to find out what are the obstacles to using infographic media in history learning at Al-Khairiyah High School Samarinda. The method used in this study is a qualitative approach with a descriptive method. Data collection is done by interview, observation and documentation. The results of the study show that the first finding is that educators easily convey learning materials and most students easily understand history lessons. While the results of the second study indicate that the obstacles faced by educators are the lack of facilities and infrastructure in schools and the limited time in preparing the material. The use of infographic media in learning history at SMA Al-Khairiyah Samarinda provides convenience in teaching and learning activities for both teachers and students.
naab: A ready-to-use plug-and-play corpus for Farsi

Huge corpora of textual data are always known to be a crucial need for training deep models such as transformer-based ones. This issue is emerging more in lower resource languages - like Farsi. We propose naab, the biggest cleaned and ready-to-use open-source textual corpus in Farsi. It contains about 130GB of data, 250 million paragraphs, and 15 billion words. The project name is derived from the Farsi word NAAB K which means pure and high grade. We also provide the raw version of the corpus called naab-raw and an easy-to-use preprocessor that can be employed by those who wanted to make a customized corpus.
Understanding Diversity in Session-Based Recommendation

Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of diversity. Besides, the asserted "trade-off" relationship between accuracy and diversity has been increasingly questioned in the literature. Towards the aforementioned issues, we conduct a holistic study to particularly examine the recommendation performance of representative SBRSs w.r.t. both accuracy and diversity, striving for better understanding the diversity-related issues for SBRSs and providing guidance on designing diversified SBRSs. Particularly, for a fair and thorough comparison, we deliberately select state-of-the-art non-neural, deep neural, and diversified SBRSs, by covering more scenarios with appropriate experimental setups, e.g., representative datasets, evaluation metrics, and hyper-parameter optimization technique. Our empirical results unveil that: 1) non-diversified methods can also obtain satisfying performance on diversity, which might even surpass diversified ones; and 2) the relationship between accuracy and diversity is quite complex. Besides the "trade-off" relationship, they might be positively correlated with each other, that is, having a same-trend (win-win or lose-lose) relationship, which varies across different methods and datasets. Additionally, we further identify three possible influential factors on diversity in SBRSs (i.e., granularity of item categorization, session diversity of datasets, and length of recommendation lists).
Editorial of ARIS2 - Advanced Research on Information Systems Security, an International Journal

As the year 2022 enters its middle range, the world is assisting an abnormal event with a heavy impact on cyber security, the Russian-Ukraine conflict. Since the beginning of this conflict that two immense groups have been created and explore security flaws within each side and to do so with success, they resort to vulnerabilities, coercion, or social engineering techniques. Protecting information, actors, and their frameworks has emerged as a critical struggle and science has an immense role in performing and maintaining security actors one step forward in this scenario.
Estimation of optimal control for two-level and three-level quantum systems with bounded amplitude

A systematic scheme is proposed to numerically estimate the quantum speed limit and temporal shape of optimal control in two-level and three-level quantum systems with bounded amplitude. For the two-level system, two quantum state transitions are studied as illustration. Comparisons between numerical and analytical results are made, and deviations are significantly small. For the three-level system, two critical time points are determined with high accuracy, and optimal controls are obtained for different durations. The shape of optimized control field is simple and does not switch frequently, thus are easy to implement in experiment. In addition, we compare our method with the chopped random basis (CRAB), and the performance of our method is much better than that of CRAB. Our scheme is of importance in estimating the quantum speed limit and optimal control for cases in which analytical solution is absent.
DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.
Reduce the effort and time in calculating the absenteeism percentage of students using Internet of Thing (IOT) technology

This study started from some of the problems that took from our institution a lot of time to solve, this study included two parts, and the first part is Image discrimination problems are usually difficult to solve using unprocessed data. to improve the discrimination process Characteristic extraction is often needed to represent data in a space of distinction. Pattern recognition aims to find or recognize specific patterns or structures in digital images (signal). To identify a pattern or an object in an image, you must first obtaining direct preliminary and statistical information about the image that must be digital in order to be able to deal with it by computer. The information in the image must also be classified to facilitate subsequent operations on the image, such as Debriefing. The second part is to calculate the absences of students with an electronic system based on two languages ​​where VB.Net language was adopted to accomplish the program interfaces in addition to the special code to find the percentage of absences, SQL was adopted for the purpose of creating a database in the program and storing data. The process of finding a mechanism to link the VB.Net program with the Internet for sending an e-mail message to the person concerned with the program (the student) for informing the percentage of absences in the study materials when exceeded the ratio approved in the program. The program has also been linked to a local network to be used on more than one computer at the same time and on a single database.
Tracking the object analysis movement in position data map in the Surveillance of Data object

Abstract Analysis of object tracking in the movement of order position with an sequence object that coordinate the mapped function, in order to position the object sequence that arrange the values in the trained dataset. Order Value in the coordinate function map in the position order value made in the dataset to the arranging object, the value render the movement of one positional order into the sequence of other object position. Priority Order in the functional variable that range the order sequence movement that incurs the evolution position in the trained dataset, order in the value sequence that position the coordinate map with the trained dataset. Mapping order value in the position sequence that prioritize the value position of gyration with the order movement that position the value in the priority sequence in the movement from the order position. In the Existing System pre-trained order classifier that can be used to detect objects such as sapling, registration mark, phizog, crevice aperture In the Proposed method to attain the huge computational power ,in order to made in the evaluation of computationally feasible, it incurs the focus in the object movement tracking sequence in the surveillance order
Loading Probability Distributions in a Quantum circuit

Quantum circuits generating probability distributions has applications in several areas. Areas like finance require quantum circuits that can generate distributions that mimic some given data pattern. Hamiltonian simulations require circuits that can initialize the wave function of a physical quantum system. These wave functions, in several cases, are identical to some very well known probability distributions. In this paper we discuss ways to construct parameterized quantum circuits that can generate both symmetric as well as asymmetric distributions. We follow the trajectory of quantum states as single and two qubit operations get applied to the system, and find out the best possible way to arrive at the desired distribution. The parameters are optimized by a variational solver. We present results from both simulators as well as real IBM quantum hardwares.
Lattice Codes for Lattice-Based PKE

Existing error correction mechanisms in lattice-based public key encryption (PKE) rely on either naive modulation or its concatenation with error correction codes (ECC). This paper shows that lattice coding, as a joint ECC and modulation technique, can substitute the naive modulation in existing lattice-based PKEs to enjoy better correction performance. We begin by modeling the FrodoPKE protocol as a noisy point-to-point communication system, where the communication channel is similar to the additive white Gaussian noise (AWGN) channel. To employ lattice codes for this special channel that hinges on hypercube shaping, we propose an efficient labeling function that converts between binary information bits and lattice codewords. The parameter sets of FrodoPKE are improved towards either higher security levels or smaller ciphertext sizes. For example, the proposed Frodo-1344-E$_\text{8}$ has a 10-bit classical security gain over Frodo-1344.
Relationship between Learning Environment with Civic-Educational Behaviors from the Students᾽ viewpoint

Relationship between Learning Environment with Civic-Educational Behaviors from the Students᾽ viewpoint
L2 ASD Learners’ Scaffolding Development after Long Interaction with The Mobile Story-Sharing Application

L2 ASD Learners’ Scaffolding Development after Long Interaction with The Mobile Story-Sharing Application
Artificial Neural Network (ANN) Implementation with Conjugate Gradient Algorithm to Predict Sumatran Melinjo Plant Production

Melinjo is an annual plant with open seeds. Tree-shaped and has two houses called dioecious or there are males and females. Melinjo is often found in dry and tropical areas. Indonesia can be one that produces melinjo as a trade product in large quantities. Melinjo is collected and shipped natural products after 5-6 long time after sowing of seeds. In West Sumatra, it is detailed that each year produces 20,000 to 25,000 natural melinjo products and the seed generation reaches 80 to 100 kg per tree per year. Therefore, it is important to know every need for melinjo by anticipating the number of generations of Melinjo using a Manufacturing Artificial Neural System with Backpropagation strategy. With the neural structure made, it will be easier to carry out this investigation. Where the machine learning method can help to find the best performance value and value from the simple data studied. The Matlab2011b application has a feature that helps to calculate the best performance and value with the help of the Conjugate Gradient algorithm. After testing using 5 samples, namely: 4-10-1, 4-15-1, 4-20-1,4-25-1, 4-30-1. Of the five tests, the best results are on data 4-15-1 with the MSE/Performance value of 0.011154591. 4-15-1, 4-20-1,4-25-1, 4-30-1. Of the five tests, the best results are on data 4-15-1 with the MSE/Performance value of 0.011154591. 4-15-1, 4-20-1,4-25-1, 4-30-1. Of the five tests, the best results are on data 4- 15-1 with the MSE/Performance value of 0.011154591.