Reviewerly/exHarmony huggingface dataset:
Kub-Sec, an automatic Kubernetes cluster AppArmor profile generation engine

Kubernetes (K8s) is one of the best options available to deploy applications in large-scale infrastructures. Security has been a big concern for all practitioners in the K8s eco-system. Almost all cloud vendors have their security solution for K8s cluster, pods, workloads, etc. In recent years, a large number of open-source tools and projects related to K8s security have emerged to meet the increased demand for enhanced security in these systems. Following this general need and trend, we propose a new design for automatic K8s cluster AppArmor profile generation. Our design is based on a most recent work of automatic AppArmor policy generator for Docker containers called Lic-Sec. The system collects the behavioral data of application containers in all worker nodes distributively, then centrally transforms the data to AppArmor policies for each application container, and enforces the policies without interrupting the service. We present a prototype of the system using Google K8s environment and with an AppArmor profile for a WordPress personal blog. We show that the security policies generated by the system can defend one typical kind of attack which targets all WordPress's XML-RPC interface.
Neural Reference Synthesis for Inter Frame Coding

This work proposes the neural reference synthesis (NRS) to generate high-fidelity reference block for motion estimation and motion compensation (MEMC) in inter frame coding. The NRS is comprised of two submodules: one for reconstruction enhancement and the other for reference generation. Although numerous methods have been developed in the past for these two submodules using either handcrafted rules or deep convolutional neural network (CNN) models, they basically deal with them separately, resulting in limited coding gains. By contrast, the NRS proposes to optimize them collaboratively. It first develops two CNN-based models, namely EnhNet and GenNet. The EnhNet only uses spatial correlations within the current frame for reconstruction enhancement and the GenNet is then augmented by further aggregating temporal correlations across multiple frames for reference synthesis. However, a direct concatenation of EnhNet and GenNet without considering the complex temporal reference dependency across inter frames would implicitly induce iterative CNN processing and cause the data overfitting problem, leading to visually-disturbing artifacts and oversmoothed pixels. To tackle this problem, the NRS applies a new training strategy to coordinate the EnhNet and GenNet for more robust and generalizable models, and also devises a lightweight multi-level R-D (rate-distortion) selection policy for the encoder to adaptively choose reference blocks generated from the proposed NRS model or conventional coding process. Our NRS not only offers state-of-the-art coding gains, e.g., >10% BD-Rate (Bjøntegaard Delta Rate) reduction against the High Efficiency Video Coding (HEVC) anchor for a variety of common test video sequences encoded at a wide bit range in both low-delay and random access settings, but also greatly reduces the complexity relative to existing learning-based methods by utilizing more lightweight DNNs. All models are made publicly accessible at https://github.com/IVC-Projects/NRS for reproducible research.
Smoke testing for machine learning: simple tests to discover severe bugs

Abstract Machine learning is nowadays a standard technique for data analysis within software applications. Software engineers need quality assurance techniques that are suitable for these new kinds of systems. Within this article, we discuss the question whether standard software testing techniques that have been part of textbooks since decades are also useful for the testing of machine learning software. Concretely, we try to determine generic and simple smoke tests that can be used to assert that basic functions can be executed without crashing. We found that we can derive such tests using techniques similar to equivalence classes and boundary value analysis. Moreover, we found that these concepts can also be applied to hyperparameters, to further improve the quality of the smoke tests. Even though our approach is almost trivial, we were able to find bugs in all three machine learning libraries that we tested and severe bugs in two of the three libraries. This demonstrates that common software testing techniques are still valid in the age of machine learning and that considerations how they can be adapted to this new context can help to find and prevent severe bugs, evenin mature machine learning libraries.
Disagreement-based class incremental random forest for sensor-based activity recognition

Activity recognition plays a key role in many fields, such as health monitoring and elderly care. Handling changes in user habits is a significant technical challenge in activity recognition. Ideally, a model should adapt to newly emerging classes and concept drift dynamically. This paper proposes a novel semi-supervised class incremental learning method, namely, disagreement-based class incremental random forest (Di-CIRF). The proposed model can detect newly emerging classes and update a previously established activity recognition model through streaming data. First, it is necessary to identify novel candidates by employing the disagreement-based confidence voting mechanism and minimum bounding box (MBB)-based separation detection to annotate newly emerging data accurately. Then, the coarse coding-based cohesion detection strategy is adopted to filter out the true novelty instances. This paper also proposes the iterative MBB-based splitting strategy and the pseudo-instance generation mechanism in Di-CIRF for updating the activity model without retaining the trained data. According to experimental results on four public activity recognition datasets, Di-CIRF outperforms the state-of-the-art methods.
Plug-and-Play video super-resolution using edge-preserving filtering

Video super-resolution objective is based on high resolution video frame reconstruction after downsampling. Such technology overcomes the inherent resolution limitations in videos. The mentioned task considers the inverse problem of original high resolution video frame recovery using prior information and reasonable suppositions. Motivated by the notable results of learning-based super-resolution strategies in obtaining high resolution outcomes from low resolution inputs, we leverage student-t mixture model as a promising reconstruction tool regarding video super-resolution. Student-t mixture model has a heavy tail which makes it robust and suitable for video frame patch prior and a rich mixture model in terms of log likelihood for information retrieval. Furthermore, in order to overcome the potential data uncertainties, edge-preserving filtering is applied to detect and preserve the video frame areas where the light sharply changes. For this purpose, we consider video frames in the framework of patches which will be selected on each frame based on their high amount of information. Afterwards, we use Plug-and-Play structure in order to apply student-t mixture prior model along with edge preserving filtering for representing the video frame patch prior in super-resolution algorithm. Finally, the proposed algorithm is evaluated empirically over 5 video frame sets, escalator, fountain, tree, wave and traffic. In addition, the results are compared with eight other state of the art super-resolution methods including SAN, RCAN, RDN, ANR, A+, SRCNN, FSRCNN-s and SelfExSR and it is proved that the proposed framework generally provides the best results in comparison with other techniques over four different super-resolution scales.
Computerization of Off-Topic Essay Detection: A possibility?

Checking essays written by students is a very time consuming task. Besides spellings and grammar, they also need to be evaluated on their semantic content such as cohesion, coherence, etc. In this study we focus on one such aspect of semantic content which is the topic of the essay. Putting it formally, given a prompt or essay-statement and an essay, this study aims to address the problem of predicting whether the essay is off-topic or not using machine learning techniques. With an increase in online learning and evaluation platforms especially during the COVID-19 pandemic, the off-topic detection system can be very useful to check essays that are mainly submitted online. In this paper, we answer the question: given a prompt and an essay written in Pakistani English, can the process of detecting whether the essay is off-topic or not be reliably and completely autonomized with zero human intervention using currently available tools and techniques? For this purpose, we explore and implement various embedding techniques proposed in recent years to extract similarity or dissimilarity features between question and response, and compare the performance of these techniques using 10 benchmark data sets and 6 classifiers. With different classifiers and different datasets along with different embeddings, we conclude that combining word mover distance, average word embeddings and idf weighted word embeddings together and then using random forest as the classifier is the best combination for off-topic essay detection. The accuracy obtained is 93.5%.
Efficient public-key authenticated deniable encryption schemes

The notion of deniable encryption (DE) was introduced to achieve secret communications in situations of coercion or bribery. Generally, it allows a user to open the same ciphertext into multiple different messages. After the initial constructions, numerous DE schemes have been proposed to improve efficiency. Whereas, all existing DE schemes under the fully deniable framework are still inefficient due to the huge communication and computation overhead. Furthermore, these DE schemes also do not provide the functionality of authentication of users (include senders and receivers), which is a necessary functionality for various application scenarios. For instance, in the setting of electronic voting, if the voting center cannot correctly verify the identities and eligibility of voters, double voting and voter impersonation cannot be prevented. This will affect the final result of the voting and destroy the fairness of the election. Moreover, due to the existence of trust problems, voters also need to confirm whether the voting center is a genuine and legal institution. In this paper, we first formalize the syntax and security notions of public-key authenticated deniable encryption, and then propose two concrete constructions under the fully deniable framework. We also prove that our DE constructions are provably secure in the random oracle model. When comparing with available DE schemes, the proposed DE ones not only provide the desired functionality of mutual authentication, but also enjoy high efficiency. Therefore, they are more suitable for practical applications.
Improving image contrast: Challenges and solutions

This article provides a comparative analysis of some algorithms that widely used nowadays for improving image contrast. While the estimating effectiveness of the algorithms, we took attention to the preserving the brightness of the image and characteristic features of the image and the increasing visual quality of the image. Criteria such as PSNR, SSI, AMBE, which assesses image quality, were also used during the experiments
DT*: Temporal Logic Path Planning in a Dynamic Environment

Path planning for a robot is one of the major problems in the area of robotics. When a robot is given a task in the form of a Linear Temporal Logic (LTL) specification such that the task needs to be carried out repetitively, we want the robot to follow the shortest cyclic path so that the number of times the robot completes the mission within a given duration gets maximized. In this paper, we address the LTL path planning problem in a dynamic environment where the newly arrived dynamic obstacles may invalidate some of the available paths at any arbitrary point in time. We present DT*, an SMT-based receding horizon planning strategy that solves an optimization problem repetitively based on the current status of the workspace to lead the robot to follow the best available path in the current situation. We implement our algorithm using the Z3 SMT solver and evaluate it extensively on an LTL specification capturing a pick-and-drop application in a warehouse environment and an office environment2. We compare our SMT-based algorithm with two carefully crafted greedy algorithms. Our experimental results show that the proposed algorithm can deal with the dynamism in the workspace in LTL path planning effectively.
Data Augmentation in Mobility Rehab Exercises of PD Patients

Human activity recognition (HAR) systems are used to monitor Parkinson’s disease (PD) patients’ mobility progress. Machine learning methods are commonly used for the development of the HAR. These methods, however, require large amount of data collected from the human subjects. Data augmentation is an affordable alternative for facilitating the development of such systems by producing similar data from actual data collected from the human subjects. In this work, three methods of data augmentation: time warping, amplitude warping and linear combination were carried out on acceleration and gyroscope signals of 6 Inertial Measurement Unit (IMU) sensors. Data was collected from a subject performing 21 therapeutic activities. To evaluate and compare the methods, a 3D human body model was utilized to visualize the motions. Then, a group of 18 individuals familiar with human motion simulation, graded the activities produced by the augmented data. The results showed that overall, time warping is the best in maintaining the structure of the activity while creating minor variations. However, it almost fails in walking activities which are more dynamic. Additionally, amplitude warping was seen to be a better choice for walking down the stairs, passing obstacle with left foot, as well as standing posture activities.
Gesture recognition system based on ultrasonic FMCW and ConvLSTM model

Ultrasonic-based technologies could provide light-independent and high-resolution solutions in no-contact human–machine interaction. This paper proposed a gesture recognition system on the basis of ultrasonic frequency modulated continuous wave (FMCW) and ConvLSTM model. It employed a hardware layout of one transmitter and three receivers spatially installed at different directions. An FMCW signal emitted by the transmitter would be detected by the receivers after reflecting from the hand. Then, the range-Doppler maps (RDMs) of the received signal were obtained by processing 2D fast Fourier transform. The spatio-temporal features of the hand were extracted from RDMs by the ConvLSTM model, to facilitate the gesture classification. Experiments showed that a fine resolution of 0.005 m in distance and 0.03 m/s in velocity for hand movement, and an accuracy of 85.7% with a small size of 50 samples training for finger-movement gestures can be achieved, which verified the feasibility of the proposed system.
Sequence-Based Searching for SynBioHub Using VSEARCH

The ability to search for a part by its sequence is crucial for a large repository of parts. Prior to this work, however, this was not possible on SynBioHub. Sequence-based search is now integrated into SynBioHub, allowing users to find a part by a sequence provided in plain text or a supported file format. This sequence-based search feature is accessible to users via SynBioHub's web interface, or programmatically through its API. The core implementation of the tool uses VSEARCH, an open source, global alignment search tool, and it is integrated into SBOLExplorer, an open source distributed search engine used by SynBioHub. We present a new approach to scoring part similarity using SBOLExplorer, which takes into account both the popularity and percentage match of parts.
Differentially private model release for healthcare applications

The evolution of technology allowed the collection of a large amount of user data, known as big data. Among all the datasets healthcare data is more sensitive. It is extremely important to protect the individual users in such datasets. Even anonymized data release is vulnerable. Hence, in this paper, we suggest a differential privacy-based model release instead of the data release. A private model release based on six machine learning classifiers namely Support Vector Machine (SVM), Random Forest algorithm, Logistic Regression, K-Nearest Neighbor, Decision Tree, and Naive Bayes are proposed. Experimental evaluation is performed using the benchmark heart disease dataset and the accuracy of the model is analyzed. The published private model can be used for the prediction of possible heart disease in patients.
PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.
Crowdsoured Technology as a Collabarative Tool for Environmental Enforcement: A Critical Review of Current Applications

The systems in which law enforcement systems operate undergoing constant changes, such as the increase in demands for effectiveness which has led to the appreciation of IT as innovative, effective, and crucial addition to the law enforcement system. The study aims to review the existing crowdsourced technology in environmental compliance and enforcement and other relevant fields. The study recommended that the Future research is required to further understand and explore the potential and efficiency of crowdsourced application in environmental enforcement.
Discursive representation of the Article 370 abrogation: A comparative CDA of the headlines of two major Indian online news publications

The Indian government on 5 August, 2019, unilaterally removed Article 370 of its constitution that provided autonomous status to the disputed region of Jammu and Kashmir. In order to pre-empt any backlash, the authorities put the entire region under strict lockdown and imposed a complete communication blackout including suspension of internet, mobile, and landline phone services. The Indian media vociferously covered the issue of higher “national interest” with no counter-narrative from local news media in the region. Using Van Djik’s socio-cognitive model, the study conducted comparative critical discourse analysis of the headlines from two major Indian online news publications; the English daily The Times of India and the Hindi daily Dainik Jagran to identify the discursive strategies adopted by these newspapers after the revocation of the Article 370. The study aimed to understand how Indian newspapers were shaping the discourse when the Indian government imposed communication restrictions and lockdown in the region. Through CDA, the study located the discursive strategies in the headlines and the ideological standpoints they reflected while covering the Article 370 controversy. The CDA found that the headline discourse in both the news publications was characterized by aggressive nationalistic assertion reinforcing domestic legitimacy for the government’s decision. The analysis further showed substantial evidence for the cultural distances between the English and Hindi language news discourse. Unlike English headlines, the Hindi headlines contained explicit linguistic subjectivities and were overtly hyperbolic in recognizing and blending itself with the nationalist assertion and socio-political expression around the abrogation of Article 370.
Object Detection using Deep Learning

Object detection is one of the most critical and challenging tasks in computer vision. It is the process of finding objects belonging to some predefined categories and determining their location in an image or video. This paper reviews deep learning-based object detection models. The paper discusses some benchmark datasets. The performance evaluation of different detectors on different datasets based on mean Average Precision (mAP) is reviewed. Object detection is used in different fields in different forms. Applications of object detection like pedestrian detection, autonomous driving, face detection, etc., are presented. Finally, the future scope is discussed to work on new techniques for object detection.
High-Capacity Steganography Using Object Addition-Based Cover Enhancement for Secure Communication in Networks

Steganography is an essential way to ensure secure communication in networks. Most steganographic algorithms imperceptibly embed secret information into an existing cover image. However, they generally cannot find a good trade-off between embedding capacity and security, as the existing covers available for users are usually far from optimal for embedding. To address this issue, instead of directly using the existing cover images, we propose a cover enhancement scheme for high-capacity image steganography, in which textured objects are generated and adaptively pasted to an existing cover based on the estimated embedding probability maps. Specifically, by estimating the embedding probability map of the cover image, we locate the high-embedding-cost region (HECR), which is inappropriate for embedding. Then, a textured object is generated by the conditional generative adversarial networks with the input of an affinely transformed object mask, and then is pasted to the located HECR for steganography. Since the image regions inappropriate for embedding are replaced by the textured object regions, the proposed scheme can provide a much higher embedding capacity for the state-of-the-art steganographic approaches. Extensive experiments demonstrate that the proposed scheme provides high embedding capacity, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , about 2.5 times higher than the state-of-the-art steganographic methods, and comparable anti-detectability to those methods.