Reviewerly/exHarmony huggingface dataset:
Label-free Poisoning Attack against Deep Unsupervised Domain Adaptation

Deep unsupervised domain adaptation (UDA) has significantly boosted the performance of deep models on different domains by transferring knowledge from a source domain to a target domain. However, its robustness against adversarial attacks has not been explored due to the challenges of highly non-convex deep models and different data distribution. In this paper, we give the first attempt to analyze the vulnerability of deep UDA and propose a label-free poisoning attack (LFPA), which injects poisoning data into the training data to mislead adaptation between the two domains without ground truth in target domain. Specifically, we design an unsupervised adversarial loss as the attack goal, in which the pseudo-labels are used to approximate the ground-truth. Since retraining the model will gradually degrade the attack performance, we also add a regularization term to the unsupervised loss, which eliminates negative interactions between the training goal and the attack goal. To accelerate the craft of poisons, we select influential samples as the initial poisons and propose a fast reverse-mode optimization method which updates poisons according to the approximate truncated gradients. Experimental results on multiple state-of-the-art deep UDA methods demonstrate the effectiveness of the proposed LFPA and the high sensitivity of UDA to poisoning attacks.
Two-view correspondence learning using graph neural network with reciprocal neighbor attention

Recent advances in two-view correspondence learning consider aggregating local contextual information from k-nearest neighbors by exploring local geometric extractors using convolution, graph or self-attention. However, there is no guarantee that the extracted local context is conducive to correspondence pruning, since there are typically over 90% outliers in the putative set which would inevitably result in contaminated neighbors and hence contaminated local context. To address this issue, in this paper, we propose the RNA-GNN, an attentional graph neural network (GNN) architecture, which can explicitly model interactions among relevant neighboring correspondences. Specifically, a reciprocal neighbor attention (RNA) module is designed to operate sparse attention on relevant neighbors only rather than all neighbors, mitigating the interference of irrelevant and redundant connectivity, and learning more compact and robust representation. In addition, considering that seeking reliable correspondences requires both local and global context, we introduce a context-aware attention module to extract global and channel-wise context by means of global weighted average pooling with weights that are estimated within the network, excluding outliers from this pooling. Extensive experiments on publicly available datasets demonstrate that RNA-GNN substantially advances current state-of-the-arts on camera pose estimation, homography estimation, and remote sensing image registration. Our code is publicly available at https://github.com/ZizhuoLi/RNA-GNN.
On the security of lightweight block ciphers against neural distinguishers: Observations on LBC-IoT and SLIM

Interest in the application of deep learning in cryptography has increased immensely in recent years. Several works have shown that such attacks are not only feasible but, in some cases, are superior compared to classical cryptanalysis techniques. However, due to the black-box nature of deep learning models, more work is required to understand how they work in the context of cryptanalysis. In this paper, we contribute towards the latter by first constructing neural distinguishers for 2 different block ciphers, LBC-IoT and SLIM that share similar properties. We then show that, unlike classical differential cryptanalysis (on which neural distinguishers are based), the position where the round keys are included in round functions can have a significant impact on distinguishing probability. We explore this further to investigate if different choices of where the round key is introduced can lead to better resistance against neural distinguishers. We compare several variants of the round function to showcase this phenomenon, which is useful for securing future block cipher designs against deep learning attacks. As an additional contribution, the neural distinguisher for LBC-IoT was also applied in a practical-time key recovery attack on up to 8 rounds. Results show that even with no optimizations, the attack can consistently recover the correct round key with an attack complexity of around 224 full encryptions. To the best of our knowledge, this is the first third-party cryptanalysis results for LBC-IoT to date.
Value Iteration Networks With Gated Summarization Module

In this paper, we address the challenges faced by Value Iteration Networks (VIN) in handling larger input maps and mitigating the impact of accumulated errors caused by increased iterations. We propose a novel approach, Value Iteration Networks with Gated Summarization Module (GS-VIN), which incorporates two main improvements: 1) employing an Adaptive Iteration Strategy in the Value Iteration module to reduce the number of iterations; and 2) introducing a Gated Summarization module to summarize the iterative process. The adaptive iteration strategy uses larger convolution kernels with fewer iteration times, reducing network depth and increasing training stability while maintaining the accuracy of the planning process. The gated summarization module enables the network to emphasize the entire planning process, rather than solely relying on the final global planning outcome, by temporally and spatially resampling the entire planning process within the VI module. We conduct experiments on 2D grid world path-finding problems and the Atari Mr. Pac-man environment, demonstrating that GS-VIN outperforms the baseline in terms of single-step accuracy, planning success rate, and overall performance across different map sizes. Additionally, we provide an analysis of the relationship between input size, kernel size, and the number of iterations in VI-based models, which is applicable to a majority of VI-based models and offers valuable insights for researchers and industrial deployment.
Communication-Efficient Federated Double Distillation in IoV

6G will be the next horizon from connected people and things to intelligence-of-everything. Machine learning (ML) combines artificial intelligence with vehicular edge computing (VEC), further releasing the potential of data. Federated learning (FL) is used to manage the collaborative training of vehicular clients and the road side unit (RSU), which can efficiently complete the distributed data processing. However, the vehicular clients upload a large number of model parameters to occupy regular communication resources, and the stability of the link cannot be guaranteed due to the mobility of the vehicles, which may lead to expensive communication overhead in VEC. Thus, this paper proposes a communication-efficient federated double distillation (FedDD) framework, which comprehensively considers the three-dimensional attributes of the vehicles and dynamically selects the cluster-heads (CHs) to improve the transmission efficiency. Then, knowledge distillation is further integrated into the federated learning to accomplish multiple rounds of parameter distillation, thereby significantly reducing the communication overhead. Experimental results show that compared to traditional FedAvg, FedDD can reduce communication overhead by three orders of magnitude. The communication overhead of FedDD is reduced by 82% compared to FTTQ, which improves the communication efficiency of FL while sacrificing a small amount of accuracy.
Visual Saliency Guided Foveated Video Compression

Video compression has become increasingly crucial as video resolution and bitrate have surged in recent years. However, most widely applied video compression methods do not fully exploit the characteristics of the Human Visual System (HVS) to reduce perceptual redundancy in videos. In this paper, we propose a novel video compression method that integrates visual saliency information with foveation to reduce perceptual redundancy. We present a new approach to subsample and restore the input image using saliency data, which allocates more space for salient regions and less for non-salient ones. We analyze the information entropy in video frames before and after applying our algorithm and demonstrate that the proposed method reduces redundancy. Through subjective and objective evaluations, we show that our method produces videos with superior perceptual visual quality. Moreover, our approach can be added to most existing video compression standards without altering their bitstream format.
Extending Implications of Ambient-Latent Space Approach with Lagrange Multiplier Method

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Thermodynamics of Quantum Information in Noisy Polarizers

The second law of thermodynamics and Landauer's erasure principle are formulated for noisy optical polarizers, providing fundamental new advances on the thermodynamic description of quantum communication devices.
TEACHING ACADEMIC WRITING IN TERTIARY EDUCATIONAL INSTITUTIONS

Modern approaches to teaching courses on academic writing at tertiary educational institutions are based on American and European traditions. The reason for their use lies in the fact that domestic students have to present their research projects at international conferences and seminars, publish the results of the projects in foreign scientific journals. Besides, mastering the skills of academic writing helps students acquire new knowledge and become more competitive professionals at the job market. The purpose of the article is to analyze modern approaches used to teach academic writing relevant for Ukrainian tertiary educational institutions. The authors have justified the use of process writing as the main way of teaching academic writing and outlined basic stages of process writing that comprise inventing, drafting, revising, and editing. At the inventing stage, students may use such techniques that make the writing process more effective as tree diagram or map, outline (traditional or rough), listing, clustering, freewriting, looping, asking questions, and cubing. The difference between reviewing and editing is given. Revising is performed at the global level and makes students look at the draft as a whole idea, while editing is done at the local level of sentence or word and means correcting lexical, grammatical, punctuation, and stylistic errors.
Learning-based model for spot size estimation using wide-angle design radial distortion

Data-driven approaches have proven to be very efficient in many vision tasks, and they are now used for optical parameters’ optimization for application-specific camera designs. Methods such as neural networks are used to estimate camera performance indicators related to the point spread function—such as the root mean square (RMS) spot size—from optical parameters. Such procedures help to understand the connection between optical characteristics and push optical design expertize beyond its limits. We investigate these approaches to model the interaction between the distortion of wide-angle designs and their RMS spot size, which is not explained by aberration theory. Specifically, we test off-the-shelf data-driven methods to determine in which conditions we can establish a model that is able to predict the variations of the RMS spot size along the field of view from the distortion function even in the absence of a mathematical model. Although current methods focus on building accurate models often usable for very specific designs—composed of a few elements only, we present a methodology focusing on more complex and realistic wide-angle designs.
Speech Acts Analysis and Its Application Within the Interlocutors’ Communication

This research deals with Speech Acts Analysis and Its Application Within the Interlocutors’ Communication which focused on its kinds, functions, and how each kind of illocutionary acts is performed by the students in FBMB Undikma under A Pragmatic Analysis. The kinds of speech acts; locutionary acts, illocutionary acts, and perlocutionary acts. The functions of illocutionary acts; representative, directive, commissive, expressive, and declarative. A descriptive qualitative method was employed since the data being analysed was in the form of descriptive data. Content analysis method was also employed due to get description of the research phenomena which in the forms of words, clauses, phrases, and sentences. There were twenty-four fragments being analysed. As the result, based on its classification, it showed that the most dominantly performed was locutionary acts 224 times, then followed by illocutionary acts 223 times, and the last one was perlocutionary acts 215 times. The declarative sentence had the highest frequency performed 131 times and in terms of the functions; representative had the highest frequency performed 163 times. It is found that representative was the highest frequency performed by the speakers as asserting, explaining, agreeing, suggesting, asking, reporting, informing, demanding, telling, claiming, affirming, denying, disbelieving, concluding, calling, answering, introducing, greeting, and clarifying.
HYAS: Hybrid Autoscaler Agent for Apache Flink

Apache Flink is a distributed processing engine for stateful computations over unbounded and bounded data streams. Despite its versatility, Apache Flink cannot automatically and optimally adjust its computing resources to match the requirements of the incoming workload. HYAS agent monitors and models Flink's responsiveness based on changes in operator idleness, backpressure and input record lag. The decision process instructs Flink to adjust its parallelism to keep up with the workload. The performance of HYAS has been assessed experimentally on an Apache Flink deployment on Kubernetes on the Google Cloud Platform using synthetic and real-life workloads. HYAS successfully maintains application performance and provides a better performance-to-cost ratio than existing methods.
Scraping Data from Web Pages Using SPARQL Queries

Despite the increasing use of semantic data, plain old HTML web pages often provide a unique interface for accessing data from many domains. To use this data in computer applications or to integrate it with other data sources, it must be extracted from the HTML code. Currently, this is typically done by single-purpose programs called scrapers. For each data source, specific scrapers must be created, which requires a thorough analysis of the source page’s implementation in HTML. This makes writing and maintaining a set of scrapers a complex and time-consuming task. In this paper, we present an alternative approach that allows defining scrapers based on visual properties of the presented content instead of the HTML code structure. First, we render the source page and create an RDF graph that describes the visual properties of every piece of the displayed content. Next, we use SPARQL to query the model and extract the data. As we demonstrate with real-world examples, this approach allows us to easily define more robust scrapers that can be used across multiple web sites and that better cope with changes in the source documents.
In a Hurry: How Time Constraints and the Presentation of Web Search Results Affect User Behaviour and Experience

Time constraints are commonplace in our daily lives. While literature in recent years from the Information Retrieval (IR) community has increased our understanding of the effects of time constraints on search, practical effects on search outcomes have rarely been evaluated. Little is known about how different search interfaces influence search outcomes and experiences in time-constrained search. This constitutes a knowledge gap that we aim to address in our work. Through a pre-registered 4 $$\times $$ 4 between-subjects crowdsourced user study, we investigate the influence of four different interfaces (list view, grid-based view, absence of result snippets, and linear scanning pattern view) on search outcomes and experiences under imposed time constraints (no constraint and constraints at two, five, and eight minutes). Results from our study indicate that user task performance is considerably affected by time constraints. In addition, as time constraints are tightened, a trade-off between querying rates and click depths arises. While no interaction effects between SERP interfaces and time constraints were ultimately found, findings from this study form an essential foundation for future work on how search result presentation may assist those searchers under strict time constraints.
Modern Methods and Technologies of Extracurricular Work in Multicultural Communities of Russian Universities

The paper analyzes the key methods and technologies of extracurricular work currently used in higher education institutions. The authors present the major literature review on various aspects of the theory of education, as well as existing approaches in this field. Also, using general scientific and special methods of scientific research, the authors dissect various forms of extracurricular work, the use of which is accompanied by the selection of certain technologies. The paper also considers the experience of the Financial University under the Government of the Russian Federation. The study shows the carry out extracurricular work out in numerous areas, such as support for talented youth; development of the creative potential of students, volunteering, self-governance; education of the basics of citizenship, patriotism, responsibility and promotion of a healthy lifestyle. The assay of the methods available to the university shows the most effective one is to focus efforts on three key areas: the formation of students’ individual qualities for practical activities arranging, and a student’s stimulation (or punishment). According to the study results, the authors developed practical recommendations on the use of methods and technologies of extracurricular work, as well as they presented the organizational structure, general methods and special technologies of extracurricular activities for the formation of a personality with a conscious civic position.
Post Optimal Objects in Social Design: Search Experience in Historical Retrospective

The article considers the experience of application in historical and cultural research of two key concepts of the concept of critical design by Anthony Dunne: “post-optimality”, which is used to describe aesthetics or poetics of the social process or phenomenon, and “para-functionality” characterizing not the essence of the social process, but its semantics. It has been argued that the notion of critical design applied in the field of IT-technologies can be transferred to the field of social and political design, where critical design can develop as a form of discourse — beyond the design of traditional consumer products, to model social communications. As an example, the history of coffee consumption in the USSR was chosen as a political and cultural tool that shapes the development of society at the level of behavioral patterns. The loss of critical social design in Soviet society by the culture of coffee consumption occurred in the 1980s and preceded the political collapse of the entire Soviet “project”, which allows to analyze it as a complete and complete experience of transformation of the design object.
Towards Quality Improvement and Prediction of Closed Questions on Stack Overflow

In recent years, programming-related Q &A forums like Stack Overflow are rapidly gaining popularity. For many developers, the cumulative expertise of programmers and developers is a valuable resource, but it can raise worries about the quality of shared content. To maintain quality the moderators close low-quality and irrelevant questions. As only a few users who are moderators can close questions, it is a difficult task for them. In this paper, we offer a strategy for forecasting the future state of the questions on Stack Overflow by focusing on the post’s feature extraction. We classified a question into one of the five classes according to the newly updated reasons: (1) open, (2) off-topic, (3) unclear, (4) too-broad, and (5) opinion-based. We collected our dataset from the Stack Overflow data dump and the old dataset from Kaggle. We experimented with several machine learning models and achieved state-of-the-art results using XGBoost. Our method can be used to filter questions at the posting time, reducing the moderators’ overhead. To the best of our knowledge, after the update on closing reasons, our study is the first on closed question prediction.
Face Mask Detection in Real-Time Using an Automatic Door Control System

The coronavirus (COVID-19) pandemic has disrupted the lives of humans. The epidemic forced governments all over the world to remain under lockdown in order to stop the spread of the virus. The highly transmissible variants of COVID-19 have been driving an unprecedented surge of infections globally. The viruses and infections are emerging at an alarming rate, and the safety measures are much needed. Only by wearing a face mask can one successfully prevent contracting the virus. In order to determine if someone is wearing a face mask or not in public spaces, an automatic real-time face mask recognition and alert system is required. The main objective of the study is to identify face masks in a real-time video stream using deep learning and image processing techniques with a smart door lock system, which can possibly contribute to the public health care department. To detect and classify the classes with mask and without mask, four AI models “MobileNet V2,” “Inception V2,” “VGG-16,” and “RESNET 50” were trained and tested to choose the best AI model for mask detection. After getting trained on the CNN model with the help of Python libraries Keras/Tensor Flow, performance of the 4 models is evaluated by considering the accuracy, F1-score, recall, and precision metrics. From the analysis of the four models, “MobileNet V2” has the highest accuracy, high precision, high recall, and high F1-score and takes less time. So the model is used further and is interfaced with hardware module to automate the door lock control system.
A Knowledge-Based Consultant Student System Using Reasoning Techniques for Selection of Courses in Smart University

Recently, consultant students have played a significant role in the automation of a smart university. How to deal with problems in large numbers of questions and responses is hard in engineering and science. This paper has presented reasoning techniques using a fuzzy rule-based approach considered expert’s preferences in smart university. In the proposed model, the fuzzy rules using forward chaining techniques are implemented in an automatic consultant system. To validate the proposed approach, experimental results confirm that the proposed model achieved the percentages of correct responses in the knowledge base. As compared the performance to conventional approaches using no rules, the proposed knowledge-based consultant student system performs better than that of the conventional method with its no rules applied to the knowledge base.