Reviewerly/exHarmony huggingface dataset:
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks

Trigger set-based watermarking schemes have gained emerging attention as they provide a means to prove ownership for deep neural network model owners. In this paper, we argue that state-of-the-art trigger set-based watermarking algorithms do not achieve their designed goal of proving ownership. We posit that this impaired capability stems from two common experimental flaws that the existing research practice has committed when evaluating the robustness of watermarking algorithms: (1) incomplete adversarial evaluation and (2) overlooked adaptive attacks. We conduct a comprehensive adversarial evaluation of 11 representative watermarking schemes against six of the existing attacks and demonstrate that each of these watermarking schemes lacks robustness against at least two non-adaptive attacks. We also propose novel adaptive attacks that harness the adversary's knowledge of the underlying watermarking algorithm of a target model. We demonstrate that the proposed attacks effectively break all of the 11 watermarking schemes, consequently allowing adversaries to obscure the ownership of any watermarked model. We encourage follow-up studies to consider our guidelines when evaluating the robustness of their watermarking schemes via conducting comprehensive adversarial evaluation that includes our adaptive attacks to demonstrate a meaningful upper bound of watermark robustness.
Label Enhancement by Maintaining Positive and Negative Label Relation

Label distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each label to a particular instance. But many existing datasets contain only simple logical labels, since it is difficult and time-consuming to directly obtain the label distribution. So label enhancement (LE) is proposed to convert multi-label datasets consisting of logical labels into label distribution datasets. In recently, many LE algorithms have been proposed and most of them concentrate on the fitting degree, but ignore the ordering relation between positive and negative labels. Therefore, in this paper, we propose an LE algorithm based on maintaining positive and negative label relation, which contains a novel ranking loss that can generate different penalties according to different ranking errors. Our algorithm achieves a good balance between the degree of fitting and the ordering relation. The experimental results on several real-world datasets validate the effectiveness of our method.
Work-in-Progress-Using Immersive Virtual Reality in Field Service Telecom Engineers Training

The advancements in technology and the reduction in devices' cost are propelling the adoption of immersive technologies for workplace training. This paper presents a work-in-progress immersive virtual reality (VR) simulator that aims to enhance and facilitate the hands-on training process for field service telecommunication engineers. This work-in-progress paper describes an implementation of the simulator and preliminary results of users' evaluation. Additionally, it presents a usability comparison between web-based and immersive VR training based on our previous work.
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment

Image Super Resolution (SR) finds applications in areas where images need to be closely inspected by the observer to extract enhanced information. One such focused application is an offline forensic analysis of surveillance feeds. Due to the limitations of camera hardware, camera pose, limited bandwidth, varying illumination conditions, and occlusions, the quality of the surveillance feed is significantly degraded at times, thereby compromising monitoring of behavior, activities, and other sporadic information in the scene. For the proposed research work, we have inspected the effectiveness of four conventional yet effective SR algorithms and three deep learning-based SR algorithms to seek the finest method that executes well in a surveillance environment with limited training data op-tions. These algorithms generate an enhanced resolution output image from a sin-gle low-resolution (LR) input image. For performance analysis, a subset of 220 images from six surveillance datasets has been used, consisting of individuals with varying distances from the camera, changing illumination conditions, and complex backgrounds. The performance of these algorithms has been evaluated and compared using both qualitative and quantitative metrics. These SR algo-rithms have also been compared based on face detection accuracy. By analyzing and comparing the performance of all the algorithms, a Convolutional Neural Network (CNN) based SR technique using an external dictionary proved to be best by achieving robust face detection accuracy and scoring optimal quantitative metric results under different surveillance conditions. This is because the CNN layers progressively learn more complex features using an external dictionary.
Digital Pedagogy: Experience of Advanced Training

The purpose of this study is to analyze the results of the professional development program “Digital Pedagogy of the Modern University” held at the Philosophy Department of Moscow State University. In today’s increasingly mediated everyday life by digital technologies, the Internet, and virtual communicative environments, traditional education is getting a new lease of life. While digital devices have long been an integral part of modern life, social, cultural and educational institutions are struggling to keep up with the rapid pace of change. As the coronavirus spread across the world, universities moved classes online to prevent coronavirus spread. The program “Digital Pedagogy of the Modern University” was aimed not at the permanent movement from face-to-face to online education but the desire to implement an interim solution for emergency remote teaching and discuss the challenges. During the program, the organizers considered both methodological issues of creating courses and current trends in educational development, psychological aspects of network communications in a distance learning, and much more. The emphasis was made on the reflection of moving to online teaching. The survey on the results of the program showed that the impact of the experience of distance learning was a complex phenomenon. This study may serve as a basis for further discussion.
Attribute-guided attention and dependency learning for improving person re-identification based on data analysis technology

Person re-identification (Re-ID) can determine whether a pedestrian target can be matched across diverse regions or cameras, thereby alleviating the problem between massive surveillance data and inefficient manual retrieval. Inspired by attribute-person recognition (APR) network, this paper proposes an improved Re-ID method based on attribute learning, which uses an attribute-guided attention mechanism module and an attribute dependency learning module to learn fine-grained attribute features and rich dependencies among them. After that, a joint model with the integration of attribute recognition and person identity recognition is built for end-to-end training. Experimental results show that the proposed method can effectively improve Re-ID accuracy and achieve a competitive recognition performance.
A novel rhinitis prediction method for class imbalance

Rhinitis is a prevalent respiratory disease. Clinical rhinitis instances are characterized by multi-label and class imbalance, which is difficult to be accurately classified by typical machine learning methods. We propose a cascaded under-sampling ensemble learning method (CUEL) to construct multiple batch classifiers, each of which is composed of a few base classifiers with different structures. Through batch-by-batch under-sampling, the correctly classified instances of majority class are removed, and the samples that are difficult to classify are kept to gradually reach the equalization of class imbalance. We assign different weights to each of the batch classifiers to construct the final integrated classifier. Cross validation was performed on 2231 clinical rhinitis instances from Shanghai Tongji Hospital Affiliated to Tongji University. The experiment showed that the average accuracy, true positive rate, and G-mean of the CUEL model were 90.71 %, 87.44 %, and 88.18 %, respectively. Compared to typical classifiers, the CUEL model has higher accuracy, true positive rate and lower missed diagnosis rate, and has stronger generalization performance. It can make full use of all rhinitis instances and effectively reduce the prediction deviation caused by class imbalance. Therefore, it has a good auxiliary effect for the prevention and diagnosis of clinical rhinitis. In addition, we calculate the feature importance for rhinitis features on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classifications.
Text Similarity Detection Using Machine Learning Algorithms with Character-Based Similarity Measures

Text similarity detection is one of the significant research problems in the Natural Language Processing field. In this paper, we propose an approach that uses machine learning models with seven character-based similarity measures to classify texts based on similarity. For this purpose, we use character-based similarity measures—Longest Common Substring, Longest Common Subsequence, Ratcliff/Obershelp algorithms, Jaro, Jaro–Winkler, Levenshtein, and Damerau-Levenshtein distances as input of supervised machine learning algorithms. For the similarity detection task, news articles are collected from Azerbaijani news websites and 9600 text pairs are created and manually labeled as similar and non-similar. These text pairs are processed by similarity measures to feed Machine learning algorithms—Support Vector Machine, Random Forest and Multi-layer Perceptron Neural Network. We performed a 10-fold cross-validation process on the dataset and found that the trained Neural Networks model gives the best mean accuracy (96%) in detecting similarity between two text bodies. We demonstrated that our proposed method outperforms results gained from individual character-based similarity measurement.
Voice-Controlled Intelligent Wheelchair for Quadriplegics

The major challenge faced by paralyzed people is that they depend on other people for their movement. Further, because of their failing health, there is a growing need to maintain track of the status of their health too. Also, they require assistance in normal activities in daily life. Toward addressing the above challenges, electric wheelchairs catering needs of paraplegics have been developed. However, these wheelchairs are not suitable for quadriplegics as their needs and problems differ. Apart from providing mobility, continuous monitoring of health condition will be an additional feature vital to the patient. The major objective of this paper is to design a fully automated wheelchair for quadriplegics with features to continuously monitor health of the patient and reduce the burden of performing various activities of routine life. The proposed method uses the voice-controlled movement of the wheelchair along with the inbuilt manual operation for the movement. Health monitoring unit in the system is set up to govern the health status and emergency measures and precautions. The main motive behind this paper is to design a well-equipped wheelchair which is based on current technology and can get integrated into today’s world for better application and ease of living.
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective

The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limited so far. In this paper, we first prove that the focal loss is classification-calibrated, i.e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified. However, we also prove a negative fact that the focal loss is not strictly proper, i.e., the confidence score of the classifier obtained by focal loss minimization does not match the true class-posterior probability. This may cause the trained classifier to give an unreliable confidence score, which can be harmful in critical applications. To mitigate this problem, we prove that there exists a particular closed-form transformation that can recover the true class-posterior probability from the outputs of the focal risk minimizer. Our experiments show that our proposed transformation successfully improves the quality of class-posterior probability estimation and improves the calibration of the trained classifier, while preserving the same prediction accuracy.
Designing and Development of Stemmer of Dogri Using Unsupervised Learning

Stemming and lemmatization are two significant natural language processing techniques that are extensively used in morphological analyzers and are also used in information retrieval for query processing, text summarization, word sense disambiguation, document clustering and machine translation. Most of the NLP tasks are based on rules and require the supervision of a linguist. Dogri is an Indo-Aryan language and is very rich in morphology; therefore, it requires advancements in computational approaches. In this paper, we have used the approach of unsupervised learning for the development of the stemmer of Dogri language, and percentage of accuracy achieved is 69%. It is also presumed that if language-specific rules are incorporated into the domain knowledge, the level of accuracy can be drastically increased.
A $$2^{n/2}$$-Time Algorithm for $$\sqrt{n}$$-SVP and $$\sqrt{n}$$-Hermite SVP, and an Improved Time-Approximation Tradeoff for (H)SVP

We show a $$2^{n/2+o(n)}$$ -time algorithm that, given as input a basis of a lattice $$\mathcal {L}\subset \mathbb {R}^n$$ , finds a (non-zero) vector in whose length is at most $$\widetilde{O}(\sqrt{n})\cdot \min \{\lambda _1(\mathcal {L}), \det (\mathcal {L})^{1/n}\}$$ , where $$\lambda _1(\mathcal {L})$$ is the length of a shortest non-zero lattice vector and $$\det (\mathcal {L})$$ is the lattice determinant. Minkowski showed that $$\lambda _1(\mathcal {L}) \le \sqrt{n} \det (\mathcal {L})^{1/n}$$ and that there exist lattices with $$\lambda _1(\mathcal {L}) \ge \varOmega (\sqrt{n}) \cdot \det (\mathcal {L})^{1/n}$$ , so that our algorithm finds vectors that are as short as possible relative to the determinant (up to a polylogarithmic factor). The main technical contribution behind this result is new analysis of (a simpler variant of) a $$2^{n/2 + o(n)}$$ -time algorithm from [ADRS15], which was only previously known to solve less useful problems. To achieve this, we rely crucially on the "reverse Minkowski theorem" (conjectured by Dadush [DR16] and proven by [RS17]), which can be thought of as a partial converse to the fact that $$\lambda _1(\mathcal {L}) \le \sqrt{n} \det (\mathcal {L})^{1/n}$$ . Previously, the fastest known algorithm for finding such a vector was the $$2^{.802n + o(n)}$$ -time algorithm due to [LWXZ11], which actually found a non-zero lattice vector with length $$O(1) \cdot \lambda _1(\mathcal {L})$$ . Though we do not show how to find lattice vectors with this length in time $$2^{n/2+o(n)}$$ , we do show that our algorithm suffices for the most important application of such algorithms: basis reduction. In particular, we show a modified version of Gama and Nguyen's slide-reduction algorithm [GN08], which can be combined with the algorithm above to improve the time-length tradeoff for shortest-vector algorithms in nearly all regimes—including the regimes relevant to cryptography.
The ‘Unreasonable’ Effectiveness of Graphical User Interfaces for Recommender Systems

The impact of Graphical User Interfaces (GUI) for recommender systems is a little explored area. Therefore, we conduct an empirical study in which we create, deploy, and evaluate seven different GUI variations. We use these variations to display 68.260 related-blog-post recommendations to 10.595 unique visitors of our blog. The study shows that the GUIs have a strong effect on the recommender systems' performance, measured in click-through rate (CTR). The best performing GUI achieved a 66% higher CTR than the worst performing GUI (statist. significant with p<0.05). In other words, with a few days of work to develop different GUIs, a recommender-system operator could increase CTR notably – maybe even more than by tuning the recommendation algorithm. In analogy to the 'unreasonable effectiveness of data' discussion by Google and others, we conclude that the effectiveness of graphical user interfaces for recommender systems is equally 'unreasonable'. Hence, the recommender system community should spend more time on researching GUIs for recommender systems. In addition, we conduct a survey and find that the ACM Recommender Systems Conference has a strong focus on algorithms – 81% of all short and full papers published in 2019 and 2020 relate to algorithm development, and none to GUIs for recommender systems. We also surveyed the recommender systems of 50 blogs. While most displayed a thumbnail (86%) and had a mouseover interaction (62%) other design elements were rare. Only few highlighted top recommendations (8%), displayed rankings or relevance scores (6%), or offered a 'view more' option (4%).
Predicting Knowledge Gain During Web Search Based on Multimedia Resource Consumption

In informal learning scenarios the popularity of multimedia content, such as video tutorials or lectures, has significantly increased. Yet, the users' interactions, navigation behavior, and consequently learning outcome, have not been researched extensively. Related work in this field, also called search as learning, has focused on behavioral or text resource features to predict learning outcome and knowledge gain. In this paper, we investigate whether we can exploit features representing multimedia resource consumption to predict the knowledge gain (KG) during Web search from in-session data, that is without prior knowledge about the learner. For this purpose, we suggest a set of multimedia features related to image and video consumption. Our feature extraction is evaluated in a lab study with 113 participants where we collected data for a given search as learning task on the formation of thunderstorms and lightning. We automatically analyze the monitored log data and utilize state-of-the-art computer vision methods to extract features about the seen multimedia resources. Experimental results demonstrate that multimedia features can improve KG prediction. Finally, we provide an analysis on feature importance (text and multimedia) for KG prediction.
Construction of Power Communication Network Knowledge Graph with BERT-BiLSTM-CRF Model Based Entity Recognition

By extensively mining system data and integrating with artificial intelligence means, knowledge graph can be exploited in various tasks of power communication network, effectively prompting the efficiency and performance of maintenance. One of the pivotal step of the knowledge graph construction is the named entity recognition. Abundant semantic features extracted from corpus can directly improve the accuracy of resulting concepts in knowledge graph. However, existing entity recognition method is mainly based on conventional word embedding technique such as Word2Vec, which still focuses on information within single word. In this paper, we propose to construct knowledge graph with the most recently proposed BERT-BiLSTM-CRF. This model can fully consider contextual information over words and extract more semantic features for further procedures. Our experimental results on realistic maintenance data of power communication networks proved the efficacy of BERT-BiLSTM-CRF model in the construction of knowledge graph. With the assistance of knowledge graph, we build applications for two typical maintenance scenarios, process standardization and fault disposal instruction, respectively. The knowledge graph has shown promising prospect as a novel auxiliary mechanism to power communication networks.
Side-channel Resistant Implementations of a Novel Lightweight Authenticated Cipher with Application to Hardware Security

Lightweight authenticated ciphers are crucial in many resource-constrained applications, including hardware security. To protect Intellectual Property (IPs) from theft and reverse-engineering, multiple obfuscation methods have been developed. An essential component of such schemes is the need for secrecy and authenticity of the obfuscation keys. Such keys may need to be exchanged through the unprotected channels, and their recovery attempted using side-channel attacks. However, the use of the current AES-GCM standard to protected key exchange requires a substantial area and power overhead. NIST is currently coordinating a standardization process to select lightweight algorithms for resource-constrained applications. Although security against cryptanalysis is paramount, cost, performance, and resistance to side-channel attacks are among the most important selection criteria. Since the cost of protection against side-channel attacks is a function of the algorithm, quantifying this cost is necessary for estimating its cost and performance in real-world applications. In this work, we investigate side-channel resistant lightweight implementations of an authenticated cipher TinyJAMBU, one of ten finalists in the current NIST LWC standardization process. Our results demonstrate that these implementations achieve robust security against side-channel attacks while keeping the area and power consumption significantly lower than it is possible using the current standards.
Flying robot path planning techniques and its trends

A Flying robot is a system capable of vertical take-off and landing to perform some specific task with no direct human intervention. Flying robots are distinct from other robots by their ability to fly with no direct human control and are capable of making a decision based on the situation. Drones, Aerial robots, Unmanned Aerial Vehicles (UAV) are few examples of flying robots. Path determination of flying robots is one of the most critical aspects of robot Routing design. Path planning is a technique which tells flying robot how to fly, where to fly and to find a collision-free optimal path. Numerous techniques have been put forward over the past few years to find an optimal path for flying robots. This work discusses on new trends led by artificial intelligence and safe human-robot interaction for optimal path planning of aerial robots. Furthermore, a comprehensive study is carried out on various 2D and 3D path planning algorithms done to date and different algorithms are classified based on important criteria for optimal path planning for flying robots.