Reviewerly/exHarmony huggingface dataset:
Optimized Prediction of Fluency of L2 English Based on Interpretable Network Using Quantity of Phonation and Quality of Pronunciation

This paper presents results of a joint project between an engineering team of a university and an educational team of another to develop an online fluency assessment system for Japanese learners of English. A picture description corpus of English spoken by 90 learners and 10 native speakers was used, where fluency was rated by other 10 native raters for each speaker manually. The assessment system was built to predict the averaged manual scores. For system development, a special focus was put on two separate purposes. The assessment system was trained in such an analytical way that teachers can know and discuss which speech features contribute more to fluency prediction, and in such a technical way that teachers' knowledge can be involved for training the system, which can be further optimized using an interpretable network. Experiments showed that quality-of-pronunciation features are much more helpful than quantity-of-phonation features, and the optimized system reached an extremely high correlation of 0.956 with the averaged manual scores, which is higher than the maximum of inter-rater correlations (0.910).
Development of Online Home Sharing Web Application

Home Sharing is a web-based lodging system that will provide homestay or renting system for tourism in Bangladesh. Our website will be a platform that will connect those users who are willing to rent their rooms and users who are searching for a home to stay. Our platform will not own any of the real estates. Our goal is to make a responsive and user-friendly website and we will build it especially for tourism purposes. In order to build our website, we have used HTML, CSS, Bootstrap, JavaScriptfor the font-end side and Php, MySQL for the back-end side of the website. Currently in Bangladesh there are no such websites like Home sharing. Airbnb is currently approaching towards our country, but they have not been able to cover most of the areas yet. Our website will be much more helpful for those people who are visiting a place for the first time and through our website they will be able to find the best available home stay at a low cost.
Comments on “Dropping Activation Outputs with Localized First-Layer Deep Network for Enhancing User Privacy and Data Security”

Inference based on deep learning models is usually implemented by exposing sensitive user data to the outside models, which of course gives rise to acute privacy concerns. To deal with these concerns, Dong <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> recently proposed an approach, namely the dropping-activation-outputs (DAO) first layer. This approach was claimed to be a non-invertible transformation, such that the privacy of user data could not be compromised. However, In this paper, we prove that the DAO first layer, in fact, can generally be inverted, and hence fails to preserve privacy. We also provide a countermeasure against the privacy vulnerabilities that we examined.
Faster Homomorphic Trace-Type Function Evaluation

Homomorphic encryption enables computations over encrypted data without decryption, and can be used for outsourcing computations to some untrusted source. In homomorphic encryption based on the hardness of ring-learning with errors, offering promising security and functionality, a plaintext is represented by a polynomial. A plaintext is treated as a vector whose homomorphic evaluation enables component-wise addition and multiplication, as well as rotation across the components. We focus on a commonly used and time-consuming subroutine that enables homomorphically summing-up the components of the vector or homomorphically extracting the coefficients of the polynomial, and call it homomorphic trace-type function. We improve the efficiency of the homomorphic trace-type function evaluation. The homomorphic trace-type function evaluation is performed by repeating homomorphic rotation followed by addition (rotations-and-sums). To correctly add up a rotated ciphertext and an unrotated one, a special operation called key-switching should be performed on the rotated one. As key-switching is computationally expensive, the rotations-and-sums is inherently inefficient. We propose a more efficient trace-type function evaluation by using loop-unrolling, which is compatible with other optimization techniques such as hoisting, and can exploit multi-threading. We show that the rotations-and-sums is not the optimal solution in terms of runtime complexity and that a trade-off exists between time and space. Experimental results demonstrate that our proposed method works 1.32–2.12 times faster than the previous method.
GBDT Modeling of Deep Reinforcement Learning Agents Using Distillation

In recent years, the field of deep reinforcement learning has been developed, and it has become possible to learn complex task processing automatically. However, it is difficult to verify the safety of deep reinforcement learning methods because their contents are not clear, and it is difficult to find a way to improve them. Therefore, in this study, we aimed to transform the solution obtained internally by deep reinforcement learning into a GBDT model using distillation, which is commonly used in the imaging field. This method enables us to confirm the validity of the obtained model by converting the solution represented by the neural network into a GBDT model, which reduces the number of calculations and speeds up the processing time.
Sophisticated Hate Stratagems: Unpacking the Era of Distrust

Over the past decade, the rise of political extremism and its associated linguistic expression resulted in communication companies’ decisions to restrict hate speech and, in many cases, ban speech emanating from specific users. Before we attempt to regulate expression per se—whether through “cancelling” expression, “deplatforming” speakers through suspensions or platform restrictions, rewriting social media terms of service, or criminalizing harmful speech—we should seek a clearer understanding of how hate appeals are used to accomplish particular communication purposes. In this analysis, we analyze hate speech as a stratagem—an artifice or trick of war—used with great effect during the 2020 election. Our concern is how this tactic is used to harm the body politic, reducing citizen ability to engage with divergent publics and points of view, and threatening democratic rule. Critically, we must understand how communication on social media platforms is being used to destabilize the communication environment and prevent the robust discussion of ideas in a public forum, a prerequisite for democratic governance.
Global convergence of Negative Correlation Extreme Learning Machine

Ensemble approaches introduced in the Extreme Learning Machine (ELM) literature mainly come from methods that relies on data sampling procedures, under the assumption that the training data are heterogeneously enough to set up diverse base learners. To overcome this assumption, it was proposed an ELM ensemble method based on the Negative Correlation Learning (NCL) framework, called Negative Correlation Extreme Learning Machine (NCELM). This model works in two stages: i) different ELMs are generated as base learners with random weights in the hidden layer, and ii) a NCL penalty term with the information of the ensemble prediction is introduced in each ELM minimization problem, updating the base learners, iii) second step is iterated until the ensemble converges. Although this NCL ensemble method was validated by an experimental study with multiple benchmark datasets, no information was given on the conditions about this convergence. This paper mathematically presents the sufficient conditions to guarantee the global convergence of NCELM. The update of the ensemble in each iteration is defined as a contraction mapping function, and through Banach theorem, global convergence of the ensemble is proved.
Contract killings: a crime script analysis

Abstract This article contributes to our existing knowledge through a crime script analysis of contract killings, based on six extensively analyzed police investigations in the Netherlands. Starting from a universal crime script, a more specific crime script for contract killings is elaborated. To provide a clear picture of the whole process, the description of the scenes focuses on requirements, facilitators, modi operandi, and preparatory actions. A comparison of liquidation investigations is made through a crime script analysis, which results in three types of requirements: vehicles, automatic weapons, and technical equipment, including PGP-telephones and beacons. In addition, spy shops turn out to play a major role in liquidations as facilitators. Due to a lack of licensing and regulations, the owners of spy shops can decide to a large extent on their own procedures. This leads to the possibility of buying and selling equipment anonymously and with large amounts of cash, which facilitates the preparation of liquidations and crime in general. Hitmen are the second type of important facilitators. The analysis reveals that all liquidation investigations contain indications of a principal to whom account has to be held. Two investigations clearly demonstrate financial rewards for contract killings.
Fast algorithm for anchor graph hashing

Anchor graph hashing is used in many applications such as cancer detection, web page classification, and drug discovery. It computes the hash codes from the eigenvectors of the matrix representing the similarities between data points and anchor points; anchors refer to the points representing the data distribution. In performing an approximate nearest neighbor search, the hash codes of a query data point are determined by identifying its closest anchor points. Anchor graph hashing, however, incurs high computation cost since (1) the computation cost of obtaining the eigenvectors is quadratic to the number of anchor points, and (2) the similarities of the query data point to all the anchor points must be computed. Our proposal, Tridiagonal hashing , increases the efficiency of anchor graph hashing because of its two advances: (1) we apply a graph clustering algorithm to compute the eigenvectors from the tridiagonal matrix obtained from the similarities between data points and anchor points, and (2) we detect anchor points closest to the query data point by using a dimensionality reduction approach. Experiments show that our approach is several orders of magnitude faster than the previous approaches. Besides, it yields high search accuracy than the original anchor graph hashing approach.
Cardinality Estimation of an SQL Query Using Recursive Neural Networks

To learn complex interactions between predicates and accurately estimate the cardinality of an SQL query, we develop a novel framework based on recursive tree-structured neural networks, which take into account the natural properties of logical expressions: compositionality and n-ary associativity. The proposed architecture is an extension of MSCN (multi-set convolutional network) for queries containing both conjunction and disjunction operators. The goal is to represent an arbitrary logical expression in a continuous vector space by combining sub-expression vectors according to the operator type. We compared the proposed approach with the histogram-based approach on the real-world dataset and showed that our approach significantly outperforms histograms with a large margin.
Comparison between genetic algorithm with Differential Evolution in study scheduling

Abstract This paper proposed to discuss the complexity of scheduling by comparing two optimization methods between genetic algorithms with differential Evolution. Genetic Algorithms can solve the simplest to complex problems as well. Therefore, the Genetic algorithm is precisely applied to the scheduling of subjects. Then another appropriate optimization method for completing optimization is the Differential Evolution (DE) algorithm. DE algorithm is a fast and effective search algorithm in solving numerical and finding optimal global solutions. The steps of the two algorithms are initialization, participation, mutation, crossover, and selection. The scheduling system produces non-optimal schedules for teacher conflicts and empty slot schedules. After the genetic algorithm and differential evolution are applied, an analysis of the results of the subject scheduling is then performed by comparing the fitness values and the execution speed of the two algorithms. the genetic algorithm found only 2 perfect schedules out of 10 experiments, whereas in the implementation of differential algorithms, there are 7 perfect schedules out of 10 experiments. Thus, it can be concluded that by determining the value of the producing parameters 5, generation 50, mutation 0.6, and crossover 0.2, the differential evolution produces better output or conformity values using genetics.
On the Importance of Domain Model Configuration for Automated Planning Engines

The development of domain-independent planners within the AI planning community is leading to “off-the-shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach—in which planners and domain knowledge are modules of larger software applications—that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this article, we investigate how the performance of domain-independent planners is affected by domain model configuration, i.e. the order in which elements are ordered in the model, particularly in the light of planner comparisons. We then introduce techniques for the online and offline configuration of domain models, and we analyse the impact of domain model configuration on other reformulation approaches, such as macros.
Fully-Pipelined Architecture for Simulated Annealing-based QUBO Solver on the FPGA

The main contribution of this work is to propose a new fully-pipelined architecture for the QUBO solver on the FPGA. For the fully-pipelined architecture, we propose two local search algorithms based on the simulated annealing with different bit-selection strategies in the local search. The implementation supports a 1024-bit QUBO problem with 16-bit weights. For the problem, we use multiple instances and perform the local search in overlapped execution with pipeline structure. We implemented the proposed circuit on Xilinx UltraScale+ FPGA V09P. The implementation result shows that the circuit can search 3.58×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> solutions per second. In addition, by sharing the block RAM, we implemented a dual annealer architecture that has two QUBO solver into the FPGA. As a result, the dual annealer architecture can search 6.14 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> solutions per second.
Deep Learning Techniques Applied to Predict and Measure Finger Movement in Patients with Multiple Sclerosis

This research focuses on the development of a system for measuring finger joint angles based on camera image and is intended for work within the field of medicine to track the movement and limits of hand mobility in multiple sclerosis. Measuring changes in hand mobility allows the progress of the disease and its treatment process to be monitored. A static RGB camera without depth vision was used in the system developed, with the system receiving only the image from the camera and no other input data. The research focuses on the analysis of each image in the video stream independently of other images from that stream, and 12 measured hand parameters were chosen as follows: 3 joint angles for the index finger, 3 joint angles for the middle finger, 3 joint angles for the ring finger, and 3 joint angles for the pinky finger. Convolutional neural networks were used to analyze the information received from the camera, and the research considers neural networks based on different architectures and their combinations as follows: VGG16, MobileNet, MobileNetV2, InceptionV3, DenseNet, ResNet, and convolutional pose machine. The final neural network used for image analysis was a modernized neural network based on MobileNetV2, which obtained the best mean absolute error value of 4.757 degrees. Additionally, the mean square error was 67.279 and the root mean square error was 8.202 degrees. This neural network analyzed a single image from the camera without using other sensors. For its part, the input image had a resolution of 512 by 512 pixels, and was processed by the neural network in 7–15 ms by GPU Nvidia 2080ti. The resulting neural network developed can measure finger joint angle values for a hand with non-standard parameters and positions.
New Capabilities of the Geometric Multidimensional Scaling

Multidimensional scaling (MDS) is a prevalent method for presenting multidimensional data visually. MDS minimizes some stress function. We have proposed in [1] and [2] to consider the stress function and multidimensional scaling, in general, from the geometric point of view, and the so-called Geometric MDS has been developed. Geometric MDS allows finding the proper direction and step size forwards the minimum of the stress function analytically when coordinates of a separate point of the projected space are varied. In this paper, we examine the stress function theoretically and experimentally when simultaneously changing the position of all the points of the projected space in the directions, defined by the Geometric MDS strategy for a separate point. Several new properties and capabilities of the Geometric MDS have been discovered. The obtained results allow us to extend the understanding of properties and ideas of Geometric MDS for the future development of a class of new effective algorithms for multidimensional data visualization and its dimensionality reduction.
Model-Based Clustering for Cylindrical Data

The objective of this paper is to perform clustering based on data consisting of both linear and circular variables, that is the data that lie on the surface of a cylinder. There are many circular–linear distributions available in the literature. We use the pragmatic approach of specifying the conditional rather than the marginal, which is often easier. Adopting Arnold et al. (Lecture Notes in Statistics: Conditionally Specified Distributions, Springer Verlag Publisher, Berlin Heidelberg, 1992), we provide the conditional distribution of θ given x and that of x given θ. Here, a mixture model approach based on the joint distribution of the linear and the circular variable is proposed. In particular, two types of such mixture models are used. One is based on the joint distribution of the marginal distribution of the linear variable and the conditional distribution of the circular variable given the linear variable and the other vice versa. Convergence property of Expectation Maximization (EM) algorithm for the members of the curved exponential family used for our models is studied. A real-life application on meteorological data is made of the proposed approaches. Comparison of the two models is done based on this example. The distinctive and important feature of preserving the geometry of the cylindrical manifold by our clustering method and its marked deviation from that for data on \(\Re ^p\) is also revealed by this example.
Prospects of the Interdisciplinary Course “Computational Intelligence” in Engineering Education

The experience of teaching lecture courses on genetic algorithms, evolutionary computing, and computational intelligence at a number of universities in Ukraine and St. Petersburg at the undergraduate and graduate levels is presented. The content of the new course “Computational Intelligence” (CI) is presented, which covers 5 main paradigms: artificial neural networks, evolutionary computing, fuzzy systems, swarm algorithms, and artificial immune systems. All 5 paradigms are successfully applied in solving many scientific and technical problems separately. However, modern trends require the development of hybrid systems where combinations of these paradigms are used. Hybrid systems are able to take advantage of the individual strengths of specific paradigms and compensate for the shortcomings of various CI paradigms. Various forms of hybridization of the components of computational intelligence are considered. On this basis, new powerful algorithms are developed to solve complex problems. The interdisciplinary character of CI is noted, which determines the prospects for its application in the development of cyber-physical systems using the processes of generalization, abstraction, and association inspired by nature. CI can be an important tool for multidisciplinary education and research.
ConTrib: Maintaining fairness in decentralized big tech alternatives by accounting work

“Big Tech” companies provide digital services used by billions of people. Recent developments, however, have shown that these companies often abuse their unprecedented market dominance for selfish interests. Meanwhile, decentralized applications without central authority are gaining traction. Decentralized applications critically depend on its users working together. Ensuring that users do not consume too many resources without reciprocating is a crucial requirement for the sustainability of such applications. We present ConTrib, a universal mechanism to maintain fairness in decentralized applications by accounting the work performed by peers. In ConTrib, participants maintain a personal ledger with tamper-evident records. A record describes some work performed by a peer and links to other records. Fraud in ConTrib occurs when a peer illegitimately modifies one of the records in its personal ledger. This is detected through the continuous exchange of random records between peers and by verifying the consistency of incoming records against known ones. Our simple fraud detection algorithm is highly scalable, tolerates significant packet loss, and exhibits relatively low fraud detection times. We experimentally show that fraud is detected within seconds and with low bandwidth requirements. To demonstrate the applicability of our work, we deploy ConTrib in the Tribler file-sharing application and successfully address free-riding behaviour. This two-year trial has resulted in over 160 million records, created by more than 94’000 users.
Improving Software Project Management by Applying Agile Methodologies: A Case Study

In the industry, the demand for building custom software is continuous and pressing; this responsibility falls directly on the institutional software provider, whether internal or external. It is common to observe that development teams have problems managing work and delays in project delivery and software delivery with quality problems during the software engineering process. Thus, this study aims to implement a methodology to improve software development management and answer the research question: What success factors were found when implementing the methodology? The research structure was established as follows: 1) Comparative analysis between agile development methodologies most used in the community. 2) Conducting a case study at Yachay Tech University 3) Determine the critical success factors in adopting the methodology. 4) Statistical analysis of the results. The results obtained from the case study's critical success factors were an improvement of 60.50% in human factors, 50.27% in organizational factors, and 32.32% in other factors.