Reviewerly/exHarmony huggingface dataset:
Customizing Feedback for Introductory Programming Courses Using Semantic Clusters

The number of introductory programming learners is increasing worldwide. Delivering feedback to these learners is important to support their progress; however, traditional methods to deliver feedback do not scale to thousands of programs. We identify several opportunities to improve a recent data-driven technique to analyze individual program statements. These statements are grouped based on their semantic intent and usually differ on their actual implementation and syntax. The existing technique groups statements that are semantically close, and considers outliers those statements that reduce the cohesiveness of the clusters. Unfortunately, this approach leads to many statements to be considered outliers. We propose to reduce the number of outliers through a new clustering algorithm that processes vertices based on density. Our experiments over six real-world introductory programming assignments show that we are able to reduce the number of outliers and, therefore, increase the total coverage of the programs that are under evaluation.
Eye Movements During Dynamic Visual Search

Human visual search performance in dynamic environment will be affected by the moving speed of background and object. Eye movement data can help us understand the reasons for this performance difference. This study investigated the changes of eye movement index of visual search under different task difficulty (high, low) and different moving speed (low, high). The results show that the moving speed of the object will affect the fixation numbers, fixation time, saccade amplitude and saccade speed. With the increase of speed, the number of fixation will increase significantly, and the fixation time, saccade amplitude and saccade speed will decrease significantly. Moreover, this index has nothing to do with the difficulty of the search task in this study. The research results can provide guidance for visual search design in dynamic environment.
Transfer Learning with Shapeshift Adapter: A Parameter-Efficient Module for Deep Learning Model

Fine-tuning pre-trained models is arguably one of the most significant approaches in transfer learning. Recent studies focus on methods whose performance is superior to standard fine-tuning methods, such as Adaptive Filter Fine-tuning and Fine-tuning last-k. The SpotTune model outperforms most common fine-tuning methods due to a novel adaptive fine-tuning approach. Since there is a trade-off between the number of parameters and performance, the SpotTune model is not parameter efficient. In this paper, we propose a shapeshift adapter module that can help reduce training parameters in deep learning models while pre-serving the high-performance merit of SpotTune. The shapeshift adapter yields a flexible structure, which allows us to find a balance between the number of parameters and performance. We integrate our proposed module with the residual blocks in ResNet and conduct several experiments on the SpotTune model. On the Visual Decathlon Challenge, our proposed method gets a score close to SpotTune and it outperforms the SpotTune model over half of the datasets. Particularly, our proposed method notably uses only about 20% of the parameters that are needed when training using a standard fine-tuning approach.
VaCSO: A Multi-objective Collaborative Competition Particle Swarm Algorithm Based on Vector Angles

Recently, particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems (MOPs). However, due to rapid convergence, PSO has poor distribution when processing MOPs. To solve the above problems, we propose a multi-objective collaborative competition particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global or individual optimal particles, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence, the population is clustered into two groups which use different learning strategies. Finally, a three-particle competition and co-evolution mechanism is proposed to improve the distribution and diversity of particle swarms. We set up comparative experiments to test the performance of VaCSO compared with the current popular multi-objective particle swarm algorithm. Experimental results show that VaCSO has excellent performance in convergence and distribution, and has a significant effect in optimizing quality.
EvolMusic

Automatic Speech Recognition (ASR) has undergone substantial improvements since the incorporation of deep learning. However, the vulnerability of neural networks to imperceptible adversarial perturbations exposes ASR-based devices to potentially serious threats. So far, imperceptibility of audio adversarial examples has been associated with small, or inaudible perturbations. In this paper, we expand the domain of viable audio adversarial examples to include audible, but inconspicuous adversarial perturbations. We present EvolMusic, the first targeted adversarial attack based on musical note-sequences. Our musical perturbations are generated via an adaptive evolutionary approach in a black-box setting. We evaluate our attack against DeepSpeech v0.9.1 using the Fluent Speech Commands dataset.
Multi-quasi-periodic Cylindrical and Circular Images

Nowadays, image processing problems are becoming increasingly important due to development of the aerospace Earth monitoring systems, medical devices, etc. But most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations, images are set on a cylinder or on a circle. The peculiarity of such images requires its consideration in their models. In the early works of the authors, several models of such images were proposed. These models can also describe quasi-periodic processes. However, these models are single-period ones. Some images and processes can have several quasi-periods. For example, seasonal within annuals, annual rings on a tree cut, etc. In the present paper, autoregressive models of multi-quasi-periodic images and processes are considered. The expressions of their covariance functions are given.
Adaptive Data Acquisition for Personalized Recommender Systems with Optimality Guarantees on Short-Form Video Platforms

The popularity of short-form video (SFV) has been exploding on digital platforms recently. The large number of videos and evolving topics on digital platforms pose technical challenges in making personalized recommendations. In this paper, we introduce a new pure exploration problem on SFV platforms for finding a $(K, \epsilon_H, \epsilon_L)$-optimal set comprising all recommendations that are within $\epsilon_L$-optimality gap and excluding all recommendations outside of the $\epsilon_H$-optimality gap, relative to the best arm with capacity at most K, for quality control. We propose an adaptive data acquisition method, called Adaptive Acquisition Tree (AAT), to jointly account for user preference heterogeneity and high-dimensional product characteristics. AAT adaptively segments users based on preference similarity and then learns a personalized transductive bandit policy that can be used on partially observed or even unobserved items to accommodate the dynamic trends on SFV platforms. We derive the sample complexity for identifying a $(K, \epsilon_H, \epsilon_L)$-optimal set for a single user and for all users, respectively. We further evaluate the algorithm via numerical experiments on data collected from the NetEase platform. Our result demonstrates that the proposed policy performs significantly better than several state-of-the-art benchmarks in four transductive scenarios for both spotlight recommendations (i.e., best-arm identifications) and $(K, \epsilon_H, \epsilon_L)$-optimal set recommendations. With the potential to improve the expected view time by 20-25%, our method produces both academic and practical value, given the increasing popularity and uniqueness of SFVs and, more broadly, user-generated content.
Checking Method for Fake News to Avoid the Twitter Effect

The recent blocking of President Trump’s twitter account has raised awareness of the danger of the impact of fake news and the importance of detecting it. Indeed, if one can doubt information, ignoring what is true or false it can lead to a loss of confidence in the decisions and other dangers. The objective of this paper is to propose an automatic method for fact checking using Knowledge Graphs, such as Wikipedia. Knowledge Graphs (KGs) have applications in many tasks such as Question Answering, Search Engines and Fact Checking, but they suffer from being incomplete. Recent work has focused on answering this problem with an abstract embedding of the KG and a scoring function, yielding results that are not easily interpretable. On the other hand, Path Ranking methods answer this problem with deductions represented by alternative paths in the KG, easily understood by a human. Favoring the Path Ranking approach for its interpretability, we propose an attention-based Path Ranking model that uses label information in the KG, making the model easily transferable between datasets, allowing us to leverage pretraining and demonstrate competitive results on popular datasets.
Novelty search for evolving interesting character mechanics for a two-player video game

Computational generation of video game content, often referred to as procedural content generation (PCG), holds much promise for generating character mechanics. Character mechanics refers to how characters are allowed to move and behave in a computer game, rather than aesthetics such as graphics and audio. In this paper we study how to generate character mechanics automatically, by means of novelty search. Our results show that some of the auto-generated characters are, by human subjects, perceived as more interesting than built-in game characters.
Influence of coin symmetry on infinite hitting times in quantum walks

Classical random walks on finite graphs have an underrated property: a walk from any vertex can reach every other vertex in finite time, provided they are connected. Discrete-time quantum walks on finite connected graphs however, can have infinite hitting times. This phenomenon is related to graph symmetry, as previously characterized by the group of direction-preserving graph automorphisms that trivially affect the coin Hilbert space. If a graph is symmetric enough (in a particular sense) then the associated quantum walk unitary will contain eigenvectors that do not overlap a set of target vertices, for any coin flip operator. These eigenvectors span the Infinite Hitting Time (IHT) subspace. Quantum states in the IHT subspace never reach the target vertices, leading to infinite hitting times. However, this is not the whole story: the graph of the 3D cube does not satisfy this symmetry constraint, yet quantum walks on this graph with certain symmetric coins can exhibit infinite hitting times. We study the effect of coin symmetry by analyzing the group of coin-permutation symmetries (CPS): graph automorphisms that act nontrivially on the coin Hilbert space but leave the coin operator invariant. Unitaries using highly symmetric coins with large CPS groups, such as the permutation-invariant Grover coin, are associated with higher probabilities of never arriving, as a result of their larger IHT subspaces.
On “Practical and secure outsourcing algorithms for solving quadratic congruences in IoTs” from IEEE IoT journal

Secure outsourced computation enables IoT devices to offload resource-intensive computations to a more resourceful server while keeping the inputs secret to the server. Recently, Zhang et al. put forth two outsourcing algorithms for solving quadratic congruences (Zhang et al., 2020). We observe that both the algorithms do not achieve the claimed security guarantees: a polynomial-time attack reveals the secret inputs to a passive adversary. As a consequence of the insecure outsourcing, the factorization of the RSA modulus is also revealed and hence leads to the total compromise of the security of the underlying scheme which makes use of the Zhang et al. outsourcing algorithms for solving quadratic congruences. Interestingly, we propose corrective measures for the Zhang et al. algorithm and prove that the resulting algorithm enables secure and verifiable delegation of solving quadratic congruences in IoTs.
Image haziness contrast metric describing optical scattering depth

Contrast is not uniquely defined in the literature. In particular, there is a need for a contrast measure that scales linearly and monotonically with the optical scattering depth of a translucent scattering layer that covers an image. Here, we address this issue by proposing an image contrast metric, which we call haziness contrast metric, and experimentally test it using milk as a scattering medium to simulate a decline in image contrast. Compared to other contrast metrics in the literature, the proposed metric is the closest to linear, as a function of the increasing density of the scattering material on the image.
Binary classification problem

Machine learning is a subfield of computer science, and binary classification is one of the most common problems in machine learning. In its basic form, binary classification refers to the model of predicting an input sample into one of two classes. This chapter first presents four main types of machine learning algorithms, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Next, we introduce the binary classification model in detail. Finally, we construct the Pokémon combat prediction model with a fully connected neural network (FCNN) step by step to help readers improve their practice skills in working with the binary classification problem and neural networks.
Distantly Supervision for Relation Extraction via LayerNorm Gated Recurrent Neural Networks

Relation extraction is a classic task in the NLP field which aims to predict the relation between two entities in given sentences. Convolutional neural network (CNN) is one of the typical neural network structures applied to this task. However, the existing CNN model used for extraction is not able to capture the time information in sentences which leads a great contribution to predict the right directionality between the two entities. Therefore, I propose a new gated recurrent neural networks with layer normalization (LNGRU) to obtain the background information of the future and the past in sentences. Experiments demonstrate that my model is effective and superior to several comparable baseline models.
Kernel Optimization in SVM for Defense Against Adversarial Attacks

While malicious samples were widely found in many application fields of machine learning, suitable countermeasures have been researched in the research field of adversarial machine learning. Support vector machines (SVMs), as a kind of successful approach, were widely used to solve security problems, such as image classification, malware detection, spam filtering, and intrusion detection. However, many adversarial attack methods have emerged recently, considering deep neural networks as machine learning models. Therefore, we consider applying them to SVMs and put forward an effective defense strategy against the attacks. In this paper, we aim to develop secure kernel machines against a prevalent attack method that was previously proposed in deep neural networks. This defense approach is based on the kernel optimization of SVMs with radial basis function kernels. To test this hypothesis, we evaluate our approach on MNIST and CIFAR-10 image classification datasets, and the experimental results show that our method is beneficial and makes our classifier more robust.
Network Security Risk Investigation System Based on Internet of Things Technology

In order to overcome the security problems in the operation of Internet platform, such as user privacy leakage, this paper proposes a network security risk investigation system based on Internet of things technology. The survey system makes full use of big data technology, cloud computing technology and other high-tech, can accurately identify the potential network security problems, and try to send the existing security problems to users and relevant departments. Based on this, the survey system is also designed as a lightweight program, which can continuously identify the security risks in the network for users in the background while occupying very little memory space. The research results show that the application of the survey system can effectively and scientifically identify network security risks, so as to provide users with a good network operating environment. At the same time, this paper also emphasizes that the premise of reducing network security accidents not only requires users to fully use the network security risk survey system, but also should establish a good network security awareness.
FUNDAMENTAL BASIS FOR IMPROVING THE METHODS OF TEACHING MATHEMATICS

To improve the methodological training system, attention should be paid to each element of its structure. Improvement of the methodological system of teaching should be based on the previously established system of teaching and upbringing of students. The general principles of improving the methodological system and the above specific provisions were implemented in the development of programs, textbooks and teaching aids on a new subject for elementary school — mathematics.
Ethical Lapses Create Complicated and Problematic Software

Creators of software must understand that their work impacts the lives of those who use it. Most of the time, the influence a particular program has on human life is minimal, but many programs impact life in profound ways. Software engineers spend their days writing new programs and fixing problems found in existing work. Often software engineers never meet any of the people who use their creations. This custom is problematic because measuring success or failure is not merely an equation of development and maintenance costs. The human factor needs to be considered. Developers should imagine the worst possible impact of their work before completing requirements and analysis, thereby setting up a greater emphasis on delivering a proper system in the first place. This paper discusses tragic failures in several projects and describes ethical lapses during development and post-delivery support. This paper's results show the important role ethics plays in software development.
Statistical Analysis of Prime Number Generators Putting Encryption at Risk

The quality of Prime Number Generators (PNGs) is measured by how complex their output is being reproduced or predicted. Several mathematical tests exist to verify the statistical behavior of PNG sequences. However, by changing this single-sided approach this unpredictability becomes more transparent by generating more than one billion primes of specific bit lengths, (32, 64, 128, etc.). Then the generated prime numbers show similar characteristics and seem somehow related to each other. This paper uses a different randomness test that shows behavior in the LibreSSL implementation indicating their PNG is not fully random. The analysis found several aspects including block patterns of same digits within the prime number itself as well as among several primes. Furthermore, the generated primes seem to be generated within a certain undocumented upper and lower boundary, which further reduces randomness. Cryptographic methods, such as RSA, rely on random primes which are now jeopardized in a different way.
Understanding and Modeling AI-Intensive System Development

Developers of AI-Intensive Systems-i.e., systems that involve both “traditional” software and Artificial Intelligence-are recognizing the need to organize development systematically and use engineered methods and tools. Since an AI-Intensive System (AIIS) relies heavily on software, it is expected that Software Engineering (SE) methods and tools can help. However, AIIS development differs from the development of “traditional” software systems in a few substantial aspects. Hence, traditional SE methods and tools are not suitable or sufficient by themselves and need to be adapted and extended.A quest for “SE for AI” methods and tools has started. We believe that, in this effort, we should learn from experience and avoid repeating some of the mistakes made in the quest for SE in past years. To this end, a fundamental instrument is a set of concepts and a notation to deal with AIIS and the problems that characterize their development processes.In this paper, we propose to describe AIIS via a notation that was proposed for SE and embeds a set of concepts that are suitable to represent AIIS as well. We demonstrate the usage of the notation by modeling some characteristics that are particularly relevant for AIIS.
Analysis of age invariant face recognition using quadratic support vector machine-principal component analysis

Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress in accuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.
K-Means Algorithm-Based Text Extraction from Complex Video Images Using 2D Wavelet

In this paper, the K-means algorithm for information extraction in the form of text from complex video images using 2D wavelet is presented. Haar wavelet and K-means algorithm-based simple hybrid approach are proposed in this paper. Haar wavelet is used to efficiently convert grayscale image to edge image. K-means algorithm is used for the information localization and segmentation process to split the background pixels from the text images. Large non-text background interrupts to extract the text information from any video images, so that non-text background can be identified and eliminated from the text images with the help of the hybrid approach. This algorithm is evaluated on the complex background video frames. The recall rate and the precision rate are obtained 99.01 and 95.75% of the proposed algorithm in video images.
The Analysis of Tuberculosis Funds Efficiency On High Burden Countries

Tuberculosis remains a major global health problem and ranks` as the second leading cause of death from infectious disease worldwide. Every country tries to spend different budget to overcome the tuberculosis problem. The level of efficiency of use of the budget will reduce the number of tuberculosis cases and give good impact to economy. This paper has two objectives. The first is to estimate technical efficiency scores of tuberculosis funds on high burden countries by using Data Envelopment Analysis (DEA) method. The second is to analyse the other environmental factors that play an important role to increase efficiency scores by using Tobit method. DEA shows some countries have high score of efficiency and some countries have low score and show how that countriesuse fund to get optimum output. The Tobit estimation shows that tax of cigarette and budget of tobacco control have positive marginal effect to technical efficiency scores.