Reviewerly/exHarmony huggingface dataset:
Learning problem generator for introductory programming courses

Automatic learning-problem generation saves effort on creating problem banks. It allows creating banks containing thousands problems, which prevents cheating, allows training until mastery, and lets intelligent tutoring systems find problems for every learning situation. We present a learning problem generator ToP, designed to generate problems for introductory programming courses from open-source code. It supports teaching determining the order of expression evaluation and program tracing. It was integrated into the CompPrehension tutor to generate initial problem banks problems and update them on the fly. It was used in studies of learning-problem generation that demonstrated that generated problems are indistinguishable from human-authored ones.
Automatic summarization of endoscopic skull base surgical videos through object detection and hidden Markov modeling

Endoscopic endonasal surgery is a medical procedure that utilizes an endoscopic video camera to view and manipulate a surgical site accessed through the nose. Despite these surgeries being video recorded, these videos are seldom reviewed or even saved in patient files due to the size and length of the video file. Editing to a manageable size may necessitate viewing 3 h or more of surgical video and manually splicing together the desired segments. We suggest a novel multi-stage video summarization procedure utilizing deep semantic features, tool detections, and video frame temporal correspondences to create a representative summarization. Summarization by our method resulted in a 98.2% reduction in overall video length while preserving 84% of key medical scenes. Furthermore, resulting summaries contained only 1% of scenes with irrelevant detail such as endoscope lens cleaning, blurry frames, or frames external to the patient. This outperformed leading commercial and open source summarization tools not designed for surgery, which only preserved 57% and 46% of key medical scenes in similar length summaries, and included 36% and 59% of scenes containing irrelevant detail. Experts agreed that on average (Likert Scale = 4) that the overall quality of the video was adequate to share with peers in its current state.
On the progression of belief

Based on weighted possible-world semantics, Belle and Lakemeyer recently proposed the logic DS, a probabilistic extension of a modal variant of the situation calculus with a model of belief. The logic has many desirable properties like full introspection and it is able to precisely capture the beliefs of a probabilistic knowledge base in terms of the notion of only-believing. While the proposal is intuitively appealing, it is unclear how to do planning with such logic. The reason behind this is that the logic lacks projection reasoning mechanisms and projection lies at the heart of planning. Projection reasoning, in general, is to decide what holds after actions. Two main solutions to projection exist: regression and progression. Roughly, regression reduces a query about the future to a query about the initial state while progression, on the other hand, changes the initial state according to the effects of actions and then checks whether the formula holds in the updated state. In this paper, we study projection by progression in the logic DS. It is known that the progression of a categorical knowledge base wrt a noise-free action corresponds to what is only-known after that action. We show how to progress a type of probabilistic knowledge base wrt noisy actions by the notion of only-believing after actions. Our notion of only-believing is closely related to Lin and Reiter's notion of progression.
Distributional generative adversarial imitation learning with reproducing kernel generalization

Generative adversarial imitation learning (GAIL) regards imitation learning (IL) as a distribution matching problem between the state-action distributions of the expert policy and the learned policy. In this paper, we focus on the generalization and computational properties of policy classes. We prove that the generalization can be guaranteed in GAIL when the class of policies is well controlled. With the capability of policy generalization, we introduce distributional reinforcement learning (RL) into GAIL and propose the greedy distributional soft gradient (GDSG) algorithm to solve GAIL. The main advantages of GDSG can be summarized as: (1) Q-value overestimation, a crucial factor leading to the instability of GAIL with off-policy training, can be alleviated by distributional RL. (2) By considering the maximum entropy objective, the policy can be improved in terms of performance and sample efficiency through sufficient exploration. Moreover, GDSG attains a sublinear convergence rate to a stationary solution. Comprehensive experimental verification in MuJoCo environments shows that GDSG can mimic expert demonstrations better than previous GAIL variants.
Demonstrativeness as a Communication Strategy in the Digital Space

The article considers the phenomenon of demonstrative behavior as a strategy of the digital space, focused on gaining a specific “socio-digital” status, for its subsequent social and economic monetization. Value and socio-psychological approaches are used. The theoretical base: the works of T. Veblen, R. Bartl, R Kyua, Nikolay I. Petev, Yuriy A. Chernavin, Dariya A. Sovaleva. Expected results: determination of motives and types of demonstrative behavior on the Internet.
A Hybrid Textual-Graphical Password Authentication System With Enhanced Security

Many positive outcomes may be attributed to the development of technology. Because of technological advancements, it is now possible to greatly simplify most human activities. The same holds for cybercriminals who, among other things, aim to get into user accounts to steal private data for their ends. Even though technological advancements are unquestionably beneficial, it is our responsibility as consumers to take all necessary precautions to safeguard our data. Various safeguards are at our disposal. The security of any given system, however, is always at risk. All of our efforts are focused on reducing the likelihood of a security breach. A textual and graphical password paradigm[1] is used for authentication here. We have settled on this framework because humans are naturally visual beings, and we think that by using a cued-recall and recognition-based strategy, we may improve a system’s defences. A password having an entropy[5] of 60 and above is said to be a strong password. This scheme of passwords once put through rigorous testing can prove to be a very good alternative to the multi-factor authentication that would require the user to wait for an OTP or some other hassle. Our approach looks to mitigate all the hassles encountered throughout the process and looks to propose a hassle-free gateway for the authentication of users. This approach helps in creating passwords having a password entropy close to 89, which is considered to be a strong password.
Semi-supervised echo state network with partial correlation pruning for time-series variables prediction in industrial processes

Abstract For an ordinary echo state network (ESN), redundant information in the huge reservoir will lead to degradation of the prediction performance of the network, especially when the labels of the samples are limited. To solve this problem, a semi-supervised ESN with partial correlation pruning (PCP-S 2 ESN) is proposed in this paper to scientifically capture the essential association between two reservoir variables while controlling for the influence of other factors. In this way, redundant neurons and their connection weights in the reservoir are eliminated, so that the prediction accuracy is significantly enhanced by optimizing the network structure. Moreover, an unsupervised pre-training procedure is introduced to modify the input weight matrix and reservoir connection weight matrix of the ESN, which successfully achieves precise prediction of time-series variables with limited labels. The superiority of the PCP-S 2 ESN model is demonstrated through two benchmark prediction tasks and the fed-batch penicillin cultivation process.
Reinforced mixture learning

In this article, we formulate the standard mixture learning problem as a Markov Decision Process (MDP). We theoretically show that the objective value of the MDP is equivalent to the log-likelihood of the observed data with a slightly different parameter space constrained by the policy. Different from some classic mixture learning methods such as Expectation-Maximization (EM) algorithm, the proposed reinforced algorithm requires no distribution assumptions and can handle the non-convex clustered data by constructing a model-free reward to evaluate the mixture assignment based on the spectral graph theory and Linear Discriminant Analysis (LDA). Extensive experiments on both synthetic and real examples demonstrate that the proposed method is comparable with the EM algorithm when the Gaussian mixture assumption is satisfied, and significantly outperforms it and other clustering methods in most scenarios when the model is misspecified. A Python implementation of our proposed method is available at https://github.com/leyuanheart/Reinforced-Mixture-Learning.
Distributional reinforcement learning with epistemic and aleatoric uncertainty estimation

Distributional reinforcement learning (RL) differs from conventional RL, which only estimates the expectation of the return. Distributional RL considers the return as a random variable and estimates its distribution. The return distribution can provide more information than its expectation in conventional RL. Thus, distributional RL has been widely studied. However, very few previous works take full advantage of the learned distribution to improve distributional RL. This paper improves distributional RL by introducing epistemic and aleatoric uncertainty estimation. First, an epistemic and aleatoric uncertainty estimation method is introduced using deep ensembles and the learned value distribution. Next, we improve the exploration efficiency of fully parametrized quantile function (FQF) for distributional RL and obtain a FQF-U (uncertainty) algorithm. Then, to overcome the problem that distributional RL cannot operate over continuous control tasks, we propose an epistemic-uncertainty-based distributional soft actor-critic algorithm with an adaptive risk-averse and risk-seeking policy. Finally, experimental results show that our algorithms outperform the baselines in Atari games and Multi-joint dynamics with contact (MuJoCo) environments.
Tracing Ip Addresses Behind Vpn/Proxy Servers

Today, in the Internet world, the volume of Internet activity is constantly increasing, generating massive amounts of data. Data has become the most powerful commodity. Every employee’s need for security and privacy is increasing dramatically. More cybersecurity and data protection is needed. One way to prevent data loss and privacy violations is to use a virtual private network. It is very challenging to read or intercept traffic sent across an encrypted “tunnel” by a virtual private network (VPN). Due to insufficient security, public networks become ideal territory for hackers. As it is necessary to distinguish between legitimate users of the system and those attempting to circumvent it, security is one of the latest advances in computer science that has received the most attention. One of the common methods is IP spoofing, in which the hackers start the attack by faking the source IP; after this event has happened, they have the ability to attack without disclosing their precise location. Majority of the time, cybercriminals will use proxy services to carry out fraudulent activities while concealing their true IP address and physical location. In an effort to escape being located and arrested by police enforcement, this is done. This project’s only goal is to stop most attempts at fraudulent transactions. This method, as described in, employs a multi-tiered detection mechanism and finds several proxy connectivity approaches. The findings of the trials show that using proxies and VPNs to detect individuals who are collecting data potentially fraudulent actions, the proxy methodology enhances corporate security.
Breast Cancerous Nucleion Digital Histogram Segmentation

Most frequent cancer which happens in women is breast cancer. One of the newest fields in today's diagnostic system is digital pathology. Image segmentation is an important step before cancer classification. When there are touching or overlapping nuclei for separation, the main issue arises. The thresholding technique is used in this thesis to present an image segmentation method. The proposed method employs OTSU thresholding followed by binarization and morphological operations. In post-processing, the unwanted region in the image is removed. The experimental results on the used datasets validate the efficiency of the suggested approach. It offers improved segmentation of breast histopathology images with high accuracy and minimal complexity as compared to state-of-the-art techniques.
An Efficient And Clever Computer-Aided Diagnosis System Design Explicitly For Mammography Examination

For women, breast cancer ranks among the most fatal diseases. Mammography is the most widely used and effective imaging technique for the initial detection of breast cancer. On a mammography, the most significant indicators of breast cancer are asymmetry, microcalcification, lumps, and distortion of the breast's architecture. What we call “microcalcification” (MC) refers to extremely minute deposits of calcium. It is common knowledge that the presence of clusters of microscopic calcification raises the cancer risk significantly. These microcalcification clusters are now being taken seriously as a reliable first signal in regular mammography screening. Microcalcifications occur in 61%-82% of malignant tumors, and in roughly 21% of these cases, they are the only indication of cancer. Microcalcifications on mammography film can be anywhere from 0.01mm2 to 1mm2. Considering how a radiologist's attention may be diverted from such minute calcium deposits. The diagnostic errors itself are the foundation of CAD in digital mammography. There are a plethora of computerized automated instruments for distinguishing and interpreting microcalcification's earliest stages. Diagnostic imaging, computer science, image processing, pattern recognition, and AI are all brought together in computer assisted diagnosis. The detection performance and screening efficacy of mammography can be improved with the help of digital image processing techniques, which CAD systems employ. CAD examines the digital mammography for suspicious regions and highlights them for the radiologist to further investigate. Once the suspicious area has been located, CAD separates it into sections, calculates many characteristics for each region, and uses the appropriate features to classify a lesion as benign or malignant.
Building customized Named Entity Recognition models for specific process automation tasks

In the context of a project aiming to build human-behaving robots for process automation, named entity recognition (NER) becomes one of the first tasks to solve. This paper presents our experience on building NER models for recognizing specific entities of interest, with the help of the state-of-the-art pre-trained BERT model. Noticing that the model built with the help of a general knowledge dataset scores poor results in retrieving entities specific to our particular use cases, we constructed two datasets tailored for our context and trained BERT-based models on it. We show that properly constructing the specific datasets is sufficient in order to obtain a good entity classification performance, without further increasing the model learning time.
Spam Detection in Text Using Machine Learning

Machine Learning is the emerging technology used for prediction. The machine takes the data and by using the machine learning algorithms, it can predictthe output. This technology is used in various fields. In this paper, we are combining the machine learning technology with one of the most importantforms of communication i.e.., SMS. During the communication, there should be safe message transfer without any interruption. This prediction model will help the user from fraudulent messages. It predicts whether the received SMS is spam or not spam. Many algorithms and techniques are available to make this prediction. Each technique is of different parameters. By reviewing these algorithms and parameters, we can get the clarity on the right way to predict and also the accuracy percentage of the each algorithm.
Nonlinear Parameter Modulation for Low-Cost Enhancement of Chaotic Entropy

In this paper, we have proposed non-linear parameter modulation as a low-cost technique to widen the chaotic region of analog one-dimensional (1-D) discrete-time chaotic maps, referred to as seed maps. The non-linear modulation circuits are designed with low transistor-count topologies. The design scheme generally applies to a wide range of 1-D discrete-time analog seed maps. The chaotic performance improvement after adding the non-linear modulation is examined with bifurcation plots and three chaotic entropy metrics namely, correlation coefficient, Lyapunov exponent, and correlation dimension. Along with the traditional design of a chaotic oscillator, two novel oscillator topologies are presented to demonstrate the versatile applicability of the proposed non-linear modulation scheme for designing chaotic systems with elevated performance. This hardware-efficient design with promising chaotic behavior shows the potential to be employed in many hardware-constrained chaos-based security applications for integrated circuits including robust random number generators, reconfigurable logic, physically unclonable function-based authentication, and so on.
Are safety-critical systems really survivable to attacks?

Safety-Critical Systems (SCS) stand for those systems designed to tackle events that could potentially cause human injury or loss of life, significant property damage, financial loss, or damage to the environment, among others. Modern SCS are in transport, infrastructure, medicine, nuclear engineering, recreation, and many other fields. Nevertheless, SCS are prone to various attacks aiming to explore their inherent complexity and broad attack surface to jeopardize essential services and assets. Survivability is key to protecting SCS through integrating preventive, reactive, and tolerant defenses. This paper stands out by analyzing the survivability aspects of SCS under attack through a systematic literature review of recently published articles. Based on the review, we devise a classification to separate the studies that focus on survivability for SCS from those that deal with related aspects, such as resistance, recognition, or tolerance to attacks. Further, we expose literature limitations indicating why there is still no guaranteed survivability of SCS in the presence of attacks.
Collaborative Edge-Cloud AI for IoT Driven Secure Healthcare System

In healthcare applications like monitoring patients in ICUs and performing precision robotic surgeries, IoT and sensor networks have become indispensable. These sensors generate a large amount of data that, when processed and visually presented to a medical professional, assists in the more accurate diagnosis and treatment of ailments. For some time now, hospital administrations have been taking advantage of public cloud(referred to as main clouds in this paper) resources to store and process patient data using the advanced AI analytical tools that these clouds provide. However, taking all the medical sensor data to the main cloud encounters network congestion and latencies that may negatively impact the outcomes. In this situation the power of edge-AI may appear appealing, but the state-of-the-art does not allow all the tasks of training complex AI models and drawing inference from them to take place at the edge. Techniques of complexity reduction like pruning and quantization have been applied to reduce storage and processing burden, but they compromise accuracy of the models. Researchers now agree on the necessity of collaborative edge-main cloud AI for demanding workloads.It is, however, necessary to realize that the multi-layer IoT-Edge-Main Cloud arrangement has an expanded attack surface. Any malicious attack on the dataflows among various layers may threaten patients’ quality of life or even their lives. Although AI can be used to secure these dataflows, using large neural network models centrally on the main cloud results in long training and inference dispersion times. We propose a collaborative, hierarchically merged technique to help train large neural network models in real-time. This is achieved by synthesizing the main cloud model using the trained layers of the edge models, resulting in a dramatic reduction in the training times of the model in the main cloud while achieving high detection accuracy. As we shall see in the description, this method removes some of the problems faced with other collaborative methods, like federated learning, which works by disaggregating models for sharing training load.
FHE-Booster: Accelerating Fully Homomorphic Execution with Fine-tuned Bootstrapping Scheduling

Fully homomorphic encryption (FHE) allows a user to outsource computation-intensive tasks to a cloud server witheut providing plaintext values or decryption heys to the server. A major drawback of these encrypted operations, however, is that they can be orders of magnitude slower than their plalintext counterparts. Moreover, because each ciphertext can only tolerate a llmited number of operatlons before the accumulated nole renders decryption impossible, an operation known as bootsirapping is needed to reduce such nolse and allow for unilimited computations. Notably, bootstrapping is signincantly slower than encrypted arithmetic operatlons, thus becoming a main performance bottleneck while evaluating FHE programs So far, the allocatlon and scheduling of bootstrapping operations has not been well Investigated, In part due to the complexity of the probkem and the difinculty in finding an optimal solution. To bridge thls gap, in thls work we formulate the bootstrapping scheduling problem and develop two Integer Programming (IP) modek. The first minimlies the number of bootstrapplng operations in an FHE program, while the second optimines the evecution time of the FHE program. We further develop two heurlstics for mapplng a target FHE program to a multi. core system in polynomial time. Our evaluation with a reallstic benchmark shows that our heuristic provides a 1.86x speedup compared to the baselline method.
Comparison of State Vector Machine and Decision Tree - Content Based Image Retrieval Algorithms to Perceive Accuracy

In recent years, there has been a significant amount of interest in Query By Image Content retrieval (QBIC). CBIR is a technique for getting images comparable to the image given as a query from a huge database. In the context of image retrieval, CBIR is more similar to human semantics. In addition to medical imaging, surveillance, weather forecasting, crime prevention, and remote sensing, the CBIR technique is used in a variety of fields. This context refers to content as the visual information in images, like texture, shape, and color. In comparison to text-based image retrieval, image content contains more information for efficient retrieval. In this study, we used Python to implement CBIR algorithms such as SVM and DT and compared their accuracy.