Reviewerly/exHarmony huggingface dataset:
Deepfake Video Detection by Using Convolutional Gated Recurrent Unit

Rapid development in deep learning is making it easier to create fake videos known as "deepfake" videos in which human faces are swapped. Since deepfake videos are difficult to recognize by human eyes, it becomes important to automatically detect these forgeries and prevent their abuse. In this paper, we propose a deep neural network model to detect deepfake videos using a convolutional neural network (CNN) to extract frame-level features. These features are then used to train a convolutional GRU that learns to distinguish between fake and real videos. Evaluation is performed on the recently released Celeb-DF(v2)datasets where a state-of-art AUC score was achieved.
Experience in Implementing Computer-Oriented Methodological Systems of Natural Science and Mathematics Research Learning in Ukrainian Educational Institutions

Taking into account the specifics of the existing dissonance regarding the technical characteristics of computer equipment used in schools in the process of designing computer-oriented methodological systems of natural science and mathematics research learning (COMSRL), the solutions which meet all the necessary requirements, contribute to the effective organization of research activities during research studies at school and are a budget alternative to expensive software are offered. The experience is analyzed. With the aim to increase the motivation and effectiveness of students’ learning the ways of solving the problems of designing variable models of research learning using the components of the COMSRL are considered. The results of the experimental study confirm that due to the use of COMSRL the optimal concentration of educational resources is ensured integrating training courses of robotics tested in the educational process and extracurricular activities; orientation of the content and technologies of students’ preparation for research, manufacturing integration in terms of educational process. To organize the students’ cognitive activity in the field of robotics, a number of constructors have been developed that help the students’ to assemble the structure, create the programme and run the robot model. The results of a wide experiment allowed us to determine the most effective psychopedagogical factors that influence the efficiency of mastering knowledge. It has been shown that in determining the feasibility of applying and selecting the COMSRL it is necessary to give preference to those ones that will help students to develop conceptual structures of knowledge.
Designing Multimodal Self-Tracking Technologies to Promote Data Capture and Self-Reflection

Self-tracking is a powerful means to help individuals monitor and improve their behaviors. While numerous tracking technologies are available, it has been challenging to lower the tracking burden whilst promoting reflection. This is because low-burden tracking technologies utilizing automated sensors reduce people’s awareness of their data; reflective tracking approaches, such as manual typing, often impose a high data capture burden. Motivated by speech input’s fast and expressive nature, my dissertation examines how speech input complements traditional touch input in supporting self-tracking. Taking a research-through-design approach, I examine the use of speech input in exercise tracking and food journaling, and evaluate a flexible multimodal self-tracking system incorporating touch and speech input in a domain-agnostic context. I hope this work can inform the design of multimodal technologies to support low-burden, engaging, and reflective self-tracking experiences.
IntuModels: Enabling Interactive Modeling for the Novice through Idea Generation and Selection

We present IntuModels, a machine-assisted interactive modeling workflow to enable the novice to create 3D models. The workflow uses a phase-driven approach, including idea generation and selection, to stimulate creativity and assist users who are not familiar with generating design ideas and 3D modeling techniques. By transforming parametric models using continual input data controlled by users, IntuModels motivates users to intuitively generate a huge amount of 3D model options with a good diversity. For selecting from the created models, we design a balanced overview showing the models using clustering and staged tools to help the user view and understand the models correctly. We tested IntuModels with a sample of novices who were asked to create a wire-based jewelry model. Presented in thematic networks and quantitative charts, the results showed that the novice considered IntuModels to be intuitive to use and useful for creating models that exceeded their expectations for post-production.
High-Quality Dialogue Diversification by Intermittent Short Extension Ensembles

Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully.Training DRL agents with diverse dialogue trajectories prepare them well for rare user requests and unseen situations.One effective diversification method is to let the agent interact with a diverse set of learned user models.However, trajectories created by these artificial user models may contain generation errors, which can quickly propagate into the agent's policy.It is thus important to control the quality of the diversification and resist the noise.In this paper, we propose a novel dialogue diversification method for task-oriented dialogue systems trained in simulators.Our method, Intermittent Short Extension Ensemble (I-SEE), 1 constrains the intensity to interact with an ensemble of diverse user models and effectively controls the quality of the diversification.Evaluations on the Multiwoz dataset show that I-SEE successfully boosts the performance of several state-of-the-art DRL dialogue agents. No DiversificationFull Diversification Quality-Controlled Diversifica.(I-SEE) User: Hi!I would like to book hotel Leverton House.System: Leverton House is a cheap ho-
The development, status and future of forensics in India

The 20th century has been called the age of science in which there have been tremendous scientific and technological developments. Forensic science institutions in India also came into being at the beginning of the 20th century. The networking of these institutions continued till the end of the 20th century and intensified in the 21st century. A lot of forensic facilities were created in India for the timely disposal of crime cases through scientific investigations in the field and the laboratories. However, the facilities created have not been commensurate with the increasing workload of crime cases. The efforts are still being made for generating further infrastructure in the workforce and machinery, science, and technology to update the forensics in India, but still, there is a further need for capacity building of forensics in India. The article highlights and discusses the contemporary scenario, problems, academic and research activities, future directions, and necessities of forensics in India.
VADETIS: An Explainable Evaluator for Anomaly Detection Techniques

Anomaly detection is a fundamental problem that consists of identifying irregular patterns that do not conform to the expected behavior of a system or the generated data. Many anomaly detection techniques have been proposed for time series data. However, selecting the most suitable detection method remains challenging as the proposed techniques widely vary in performance. The appropriate choice of a detection method impacts many properties of mission-critical applications such as in monitoring a patient's health, where anomalies are inevitable but need to be detected securely. In this demo, we present a new evaluator that allows to peruse the performance of several anomaly detection techniques and supports practitioners in understanding the behavior and (dis-)advantages of each technique for a given dataset. In a simple and well-structured way, practitioners can specify the desired anomaly detection setup, and our system would tune the parameters of each technique and analyze their properties in an easily understandable report. The tool also allows recommending the most appropriate technique for each anomaly type and evaluation metric.
LCBPA: An Enhanced Deep Neural Network-Oriented Bug Prioritization and Assignment Technique Using Content-Based Filtering

Software maintenance is an important phase of a development life cycle that needs to be essentially performed in order to avoid the software failure.To systematically handle the bugs (defects), the software development organization develops a bug report that demonstrates the vulnerabilities from the software under test.However, manually handling the bug reports is a laborious, tedious, and time-consuming task.Moreover, the bug repository receives large numbers of bug reports on daily basis, which demands to timely fix the found and received bugs.Motivated by this, current work proposes an automated bug prioritization and assignment technique, called LCBPA (Long short-term memory, Content-based filtering for Bug Prioritization and Assignment).To perform the bug prioritization, we employed Long Short-Term Memory (LSTM) to predict the priority of the bug report.In contrast, for bug assignment, we used content-based filtering, where the prioritized bug reports are automatically assigned to the developers based on their previous knowledge.The performance of the proposed bug prioritization model is determined by comparing with the state-of-the-art bug prioritization techniques, and measured using the evaluation metrics including Precision, Recall and F1-score.Similarly, the effectiveness of the bug assignment model is evaluated by defining various case scenarios.The results show that the proposed LCBPA technique outperforms the current state-of-the-art bug prioritization techniques (with a 22% increase in F1-score), and also efficiently handles the bug assignment problem compared to the existing bug assignment techniques.
A Novel Twitter Spam Detection Technique by Integrating Inception Network with Attention based LSTM

Online Social Networking sites have become a well-known way for web surfers to connect and meet. Twitter got to be a well-known micro blogging site that clients post and associate with messages known as tweets. As this networking site gains its popularity, spammers target Twitter to spread spam posts. Hence, several spam detection techniques have been proposed by analysts to create Twitter a spam-free stage. Be that as it may, the accessible machine learning algorithms cannot effectively distin- guish spammers on Twitter because of reasonable information controls by unsolicited clients to elude spam discovery. As a result, here, we present an incipient approach predicated on a deep learning technique that leverages a text-predicated feature to detect spammers. A novel architecture that contains a one-dimensional dimension reduction inception module stacked with LSTM along with an attention layer is introduced here. Within the proposed model, the inception module extricates the features from the vectors after GloVe word embedding, and then LSTM is utilized to get the context representations. An Attention layer is also used in this model to give attention to the data outputted from LSTM module. At long last, the sigmoid classifier is utilized to classify the labels as spam or ham. Here, the execution of our proposed model is compared with four machine learning-based and two deep learning-based approaches, exhibiting our approach acquired the best results with an F1-score of 95.74, accuracy of 95.75, and precision of 95.58.
Social Networking Sites’ Gag Order: Commercial Content Moderation’s Adverse Implications for Fundamental Sexual Rights and Wellbeing

This article critically investigates the reasoning behind social media content policies and opaque data politics operations regarding sexual visual social media practices and sexual talk, asking what is at stake when social media giants govern sexual sociability on an international scale. Focusing on Facebook, in particular, this article proposes an alternative perspective for handling various expressions of sexuality in social media platforms by exploring the wide-ranging ramifications of community standards and commercial content moderation policies based on them. Given that sexuality is an integral part of human life and as such protected by fundamental human rights, we endorse the freedom of expression as an essential legal and ethical tool for supporting wellbeing, visibility, and non-discrimination. We suggest that social media content policies should be guided by the interpretive lens of fundamental human rights. Furthermore, we propose that social media content policies inclusive of the option to express consent to access sexual content are more ethical and just than those structurally erasing nudity and sexual display.
Large-scale and Robust Code Authorship Identification with Deep Feature Learning

Successful software authorship de-anonymization has both software forensics applications and privacy implications. However, the process requires an efficient extraction of authorship attributes. The extraction of such attributes is very challenging, due to various software code formats from executable binaries with different toolchain provenance to source code with different programming languages. Moreover, the quality of attributes is bounded by the availability of software samples to a certain number of samples per author and a specific size for software samples. To this end, this work proposes a deep Learning-based approach for software authorship attribution, that facilitates large-scale, format-independent, language-oblivious, and obfuscation-resilient software authorship identification. This proposed approach incorporates the process of learning deep authorship attribution using a recurrent neural network, and ensemble random forest classifier for scalability to de-anonymize programmers. Comprehensive experiments are conducted to evaluate the proposed approach over the entire Google Code Jam (GCJ) dataset across all years (from 2008 to 2016) and over real-world code samples from 1,987 public repositories on GitHub. The results of our work show high accuracy despite requiring a smaller number of samples per author. Experimenting with source-code, our approach allows us to identify 8,903 GCJ authors, the largest-scale dataset used by far, with an accuracy of 92.3%. Using the real-world dataset, we achieved an identification accuracy of 94.38% for 745 C programmers on GitHub. Moreover, the proposed approach is resilient to language-specifics, and thus it can identify authors of four programming languages (e.g., C, C++, Java, and Python), and authors writing in mixed languages (e.g., Java/C++, Python/C++). Finally, our system is resistant to sophisticated obfuscation (e.g., using C Tigress) with an accuracy of 93.42% for a set of 120 authors. Experimenting with executable binaries, our approach achieves 95.74% for identifying 1,500 programmers of software binaries. Similar results were obtained when software binaries are generated with different compilation options, optimization levels, and removing of symbol information. Moreover, our approach achieves 93.86% for identifying 1,500 programmers of obfuscated binaries using all features adopted in Obfuscator-LLVM tool.
Pseudo Facial Generation with Extreme Poses for Face Recognition

Face recognition has achieved a great success in recent years, it is still challenging to recognize those facial images with extreme poses. Traditional methods consider it as a domain gap problem. Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances. Our experimental analysis shows a dramatic precision drop with extreme poses. Meanwhile, those extreme poses just exist minor visual differences after small rotations. Derived from this insight, we attempt to relieve such a huge precision drop by making minor changes to the input images without modifying existing discriminators. A novel lightweight pseudo facial generation is proposed to relieve the problem of extreme poses without generating any frontal facial image. It can depict the facial contour information and make appropriate modifications to preserve the critical identity information. Specifically, the proposed method reconstructs pseudo profile faces by minimizing the pixel-wise differences with original profile faces and maintaining the identity consistent information from their corresponding frontal faces simultaneously. The proposed framework can improve existing discriminators and obtain a great promotion on several benchmark datasets.
A Multilayered Semi-Permissioned Blockchain Based Platform for Peer to Peer Energy Trading

The recent spike in microgeneration of renewable energy is demanding a smart, reliable, secured and efficient technology to enable Peer to Peer (P2P) energy trading. Due to the inherent characteristics, blockchain has been a preferred technology for realizing P2P energy trading. However, blockchain implementations for P2P energy trading so far are suffering from critical challenges such as security, privacy and scalability. In this paper, we introduce a P2P energy trading platform that leverages the popular blockchain technology and addresses these concerns. In particular, a multilayered semi-permissioned blockchain based platform along with a Quality of Transaction (QoT) module is proposed as a trading platform that can be used for transaction of energy. A two stage blockchain architecture, backed by QoT, ensures proper verification and validation of the participants and transactions. We present two use cases that demonstrate two different attack scenarios to highlight the resiliency of the proposed framework. Finally, a qualitative analysis shows the effectiveness of the system with respect to security, privacy and scalability.
When non-elitism meets time-linkage problems

Many real-world applications have the time-linkage property, and the only theoretical analysis is recently given by Zheng, et al. (TEVC 2021) on their proposed time-linkage OneMax problem, OneMax(0,1n). However, only two elitist algorithms (1 + 1) EA and (μ + 1) EA are analyzed, and it is unknown whether the non-elitism mechanism could help to escape the local optima existed in OneMax(0,1n). In general, there are few theoretical results on the benefits of the non-elitism in evolutionary algorithms. In this work, we analyze on the influence of the non-elitism via comparing the performance of the elitist (1 + λ) EA and its non-elitist counterpart (1, λ) EA. We prove that with probability 1 - o(1) (1 + λ) EA will get stuck in the local optima and cannot find the global optimum, but with probability 1, (1, λ) EA can reach the global optimum and its expected runtime is O(n3+c log n) with [EQUATION] for the constant c ≥ 1. Noting that a smaller offspring size is helpful for escaping from the local optima, we further resort to the compact genetic algorithm where only two individuals are sampled to update the probabilistic model, and prove its expected runtime of O(n3 log n). Our computational experiments also verify the efficiency of the two non-elitist algorithms.
Recommending Videos in Cold Start With Automatic Visual Tags

This paper addresses the so-called New Item problem in video Recommender Systems, as part of Cold Start. New item problem occurs when a new item is added to the system catalog, and the recommender system has no or little data describing that item. This could cause the system to fail to meaningfully recommend the new item to the users. We propose a novel technique that can generate cold start recommendation by utilizing automatic visual tags, i.e., tags that are automatically annotated by deeply analyzing the content of the videos and detecting faces, objects, and even celebrities within the videos. The automatic visual tags do not need any human involvement and have been shown to be very effective in representing the video content. In order to evaluate our proposed technique, we have performed a set of experiments using a large dataset of videos. The results have shown that the automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags.
ExDRa: Exploratory Data Science on Federated Raw Data

Data science workflows are largely exploratory, dealing with under-specified objectives, open-ended problems, and unknown business value. Therefore, little investment is made in systematic acquisition, integration, and pre-processing of data. This lack of infrastructure results in redundant manual effort and computation. Furthermore, central data consolidation is not always technically or economically desirable or even feasible (e.g., due to privacy, and/or data ownership). The ExDRa system aims to provide system infrastructure for this exploratory data science process on federated and heterogeneous, raw data sources. Technical focus areas include (1) ad-hoc and federated data integration on raw data, (2) data organization and reuse of intermediates, and (3) optimization of the data science lifecycle, under awareness of partially accessible data. In this paper, we describe use cases, the overall system architecture, selected features of SystemDS' new federated backend (for federated linear algebra programs, federated parameter servers, and federated data preparation), as well as promising initial results. Beyond existing work on federated learning, ExDRa focuses on enterprise federated ML and related data pre-processing challenges. In this context, federated ML has the potential to create a more fine-grained spectrum of data ownership and thus, even new markets.
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions; (2) sampling examples from an approximation to the underlying data distribution; and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46% to 68%. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.