Reviewerly/exHarmony huggingface dataset:
Security in Distributed Ledger Technology: An Analysis of Vulnerabilities and Attack Vectors

Despite various security features offered by distributed ledger technology (DLT), distributed ledgers are vulnerable to a variety of malicious attacks (e.g., selfish-mining and block withholding). While such vulnerabilities have been thoroughly investigated in extant research, understanding and development of appropriate countermeasures is still in its infancy. As a steppingstone for the future development of effective countermeasures, in this paper, we investigate known attacks on distributed ledgers to consolidate the underlying attack vectors and identify the focal DLT characteristics (e.g., propagation delay or confirmation latency) that are exploited in these attack vectors. Based on these insights, we accumulate potential countermeasures proposed in extant security research. The results of our work, thereby, provide a better understanding of the vulnerabilities of distributed ledgers. This understanding supports the simulation and analysis of the system behavior of distributed ledgers and the development of new countermeasures that focus on individual DLT characteristics to impede current and future attack vectors.
Report on the future conversations workshop at CHIIR 2021

The Future Conversations workshop at CHIIR'21 looked to the future of search, recommendation, and information interaction to ask: where are the opportunities for conversational interactions? What do we need to do to get there? Furthermore, who stands to benefit? The workshop was hands-on and interactive. Rather than a series of technical talks, we solicited position statements on opportunities, problems, and solutions in conversational search in all modalities (written, spoken, or multimodal). This paper -co-authored by the organisers and participants of the workshop- summarises the submitted statements and the discussions we had during the two sessions of the workshop. Statements discussed during the workshop are available at https://bit.ly/FutureConversations2021Statements.
A Novel Recommender System Using Interest Extracting Agents and User Feedback

Recommender Systems have been used widely to provide suggestions to users based on their interests. Although many works have proposed recommender systems in the literature, the majority of these works did not consider the hidden user interests. Even those that considered the user latent interests did not resolve the conflict in interests that could occur. In our previous works, we proposed the use of intelligent agents to extract the interests of user categories such as the interests of a certain gender or the interests of those who work in a certain job. However, there could be some conflicting interests as the user would belong to many categories simultaneously. In this work, we rank the agents according to their ability to extract interests that best represent the users. For this sake, this work takes the user feedback about the most representative agent. Later, it uses classification to predict the most representative agent for new users given their user information. Experimental work proved that our method is efficient in terms of accuracy and training time.
Blockchain-based Secure Aggregation for Federated Learning with a Traffic Prediction Use Case

Federated learning is a distributed machine learning approach that can be applied to many networking applications. In this paper, we propose a novel blockchain-based secure aggregation protocol for federated learning, which simplifies the existing secure aggregation process by leveraging consensus through blockchain. We demonstrate the prototype by training a general LSTM model for traffic prediction at cell sites based on distributed time series datasets.
Fast k-means Clustering Based on the Neighbor Information

The k-means algorithm has been widely used in the last several decades, but the efficiency of Lloyd's k-means algorithm drops sharply in dealing with large-scale data scenarios. To solve this problem, this paper proposes a fast k-means algorithm based on neighbor information. Firstly, we propose a localization strategy in the reassignment step of k-means. Through this strategy, the scale of distance calculation is greatly reduced. Secondly, we propose the neighbor update strategy. In such a way, more accurate neighbors for each cluster could be found in each iteration, thereby ensuring the clustering quality when the k-means algorithm converges. The proposed k-means algorithm was evaluated on multiple real-world datasets and increased the speed up to hundreds of times while only losing about 1.10% of the clustering result quality.
Arabic Text Categorization: A Comparison Survey

Text categorization acquires more significance considering the plenty of text added continually on the web. The lack of huge and free Arabic datasets makes it more difficult to classify. This paper reviews some text classification papers with some comparisons between the datasets they used, the techniques they applied, and the best results they reached for the different methodologies that have been implemented.
FitMe: A Fitness Application for Accurate Pose Estimation Using Deep Learning

The advancements in deep learning have brought about crucial transformations in computer vision over the past two decades. Deep convolutional networks have found many applications in building fine-tuned models for implementation in vision-related tasks. Knowledge learned by deep learning models over enormous generic datasets can be transferred to be employed for much more specific tasks. In this work, are implementing the approach to provide health benefits to people. In the present work, we develop an application which help them in performing exercises without the help of a trainer and get instant feedback about the postures. We aim to make fitness accessible to all by removing barriers such as external hardware requirements and cost-based subscriptions. In this paper, we dive deep into the technical details about the application and the exact methodologies applied for building the same. Furthermore, results are evaluated after running the application over multiple scenarios and a comparative analysis is performed.
Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics

Patient similarity analytics has emerged as an essential tool to identify cohorts of patients who have similar clinical characteristics to some specific patient of interest. In this study, we propose a patient similarity measure called D3K that incorporates domain knowledge and data-driven insights. Using the electronic health records (EHRs) of 169,434 patients with either diabetes, hypertension or dyslipidaemia (DHL), we construct patient feature vectors containing demographics, vital signs, laboratory test results, and prescribed medications. We discretize the variables of interest into various bins based on domain knowledge and make the patient similarity computation to be aligned with clinical guidelines. Key findings from this study are: (1) D3K outperforms baseline approaches in all seven sub-cohorts; (2) our domain knowledge-based binning strategy outperformed the traditional percentile-based binning in all seven sub-cohorts; (3) there is substantial agreement between D3K and physicians (κ = 0.746), indicating that D3K can be applied to facilitate shared decision making. This is the first study to use patient similarity analytics on a cardiometabolic syndrome-related dataset sourced from medical institutions in Singapore. We consider patient similarity among patient cohorts with the same medical conditions to develop localized models for personalized decision support to improve the outcomes of a target patient.
Digital Knowledge Base for Industrial Augmented Reality Systems Based on Semantic Technologies

Augmented Reality is a technology that offers enormous potential in the industry. Due to a lack of expertise, however, companies are facing various challenges in exploiting this potential. This includes the demand-oriented configuration of the AR system. Depending on the planned use case and company-specific influencing factors and general conditions, suitable AR devices are to be identified and functionalities need to be purposefully selected. The necessary knowledge is implicitly available in numerous sources and difficult to access for companies. In the scope of this work, therefore, a machine-readable knowledge base is developed, in which the knowledge relevant for the AR system configuration is consolidated and formalized. Four aspects of knowledge are considered: The AR system structure including technical and functional components; Potential influences through ambient factors; Experience and application knowledge; The specification of specific AR devices. The knowledge base is realized based on the Web Ontology Language (OWL) and thus enables a digital and partially automated processing of the knowledge using software tools and algorithms. In combination with Artificial Intelligence solutions, the developed knowledge base can be transferred to other systems in the future and provide a powerful tool for system configuration.
Disentangled Item Representation for Recommender Systems

Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price, and style of clothing). Utilizing this attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item representations fail to fully exploit the rich attribute information or provide explanation in recommender systems. To this end, we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this article, where the items are represented as several separated attribute vectors instead of a single latent vector. In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation. We introduce a learning strategy, LearnDIR, which can allocate the corresponding attribute vectors to items. We show how DIR can be applied to two typical models, Matrix Factorization (MF) and Recurrent Neural Network (RNN). Experimental results on two real-world datasets show that the models developed under the framework of DIR are effective and efficient. Even using fewer parameters, the proposed model can outperform the state-of-the-art methods, especially in the cold-start situation. In addition, we make visualizations to show that our proposition can provide explanation for users in real-world applications.
Navigation in Unknown Environments Using Safety Velocity Cones

We rely on Nagumo's invariance theorem to develop a new approach for navigation in unknown environments of arbitrary dimension. The idea consists in projecting the nominal velocities (that would drive the robot to the target in the absence of obstacles) onto Bouligand's tangent cones (referred to as the safety velocity cones) when the robot is close to the boundary of the free space. The proposed projection-based controller is explicitly constructed to guarantee safety and convergence to a set of Lebesgue measure zero that contains the target. For specific free spaces such as Euclidean sphere worlds, the convergence to the target is guaranteed from almost all initial conditions in the free space. We provide a version of the controller (generating a continuous and piecewise smooth closed-loop vector field) relying on the robot's current position and local range measurements (e.g., from LiDAR or stereo vision) without global prior knowledge about the environment.
PnG: Micro-structured Prune-and-Grow Networks for Flexible Image Restoration

This paper addresses one major issue of DNN-based image restoration: the difficulty of using one model to fit multiple reconstruction requirements, such as supporting different compression ratios in neural image compression (NIC) or different zooming scales in single image super-resolution (SISR). Instead of training an independent model instance for each requirement as an individual task, we develop a practical solution that uses one model instance to support multiple requirements. We propose a general multi-task learning framework based on a novel prune-and-grow (PnG) process, where each task corresponds to each of the requirements. Different from traditional multi-task networks that use fully shared or task-specific layers, we enable in-layer partial parameter sharing to obtain both common and task-specific features at various abstraction levels. This encourages adequate sharing to improve the overall multi-task performance. The parameters are shared at a micro-structured level to both maintan the task performance and reduce inference computation. The sharing structure is automatically learned, where a model instance trained for previous tasks is progressively pruned and regrown to perform more tasks. The framework is task-generic and model-structure-agnostic. Using NIC and SISR as two example applications, extensive experiments show that the multi-task PnG network can largely reduce the overall model size and inference computation, with almost no degradation of the reconstruction performance.
Contemporary trends in privacy-preserving data pattern recognition

The article is devoted to the recent scientific problem of privacy-preserving data pattern recognition. The purposes of the work are to systematize the security models for such tasks, to identify algorithmic tools that can be used to ensure the privacy of the data processing, and application of models and to analyze the privacy-preserving data pattern recognition systems. The article presents the main concepts and some definitions related to privacy-preserving machine learning, gives a systematization of related problems, and notes modern and promising areas of development of machine learning. Special cryptographic methods and protocols are correlated to the solved problems. A brief description of the known privacy-preserving data pattern recognition systems is given. Unsolved problems in the field of privacy-preserving data pattern recognition are considered.
Hybrid Approach to Cross-Platform Mobile Interface Development for IAAS

Information Access Assistant Service (IAAS) is a collaborative filtered document recommendation system. The validation of the model was carried out by implementing the system on a library management software in a target and closed environment using Web technology. The tests are performed with computers, which limits the use of IAAS. As of today, smartphones occupy a place of choice in population’s daily life and moreover, the Web technology used does not promote a good adaptation of interfaces on the mobile. This led us to think about setting up a user interface facilitating human computer interaction on the mobile. The existence of a multitude of mobile platforms, of different sizes and regarding to the context of the use of IAAS, we adopted for cross-platform development. In this paper, we proposed to use the hybrid approach and we used ionic as an implementation tool for our mobile solution. We set up the solution and also performed display tests and evaluation in a closed environment. The test was done by setting up a server, a connection, and three smartphones. The results observed show a good adaptation of the interfaces on the different smartphones but also in relation to the Web interfaces.
Detecting Image Similarity Using SIFT

Manually identifying similarity between any images is a bit difficult task. So, we have come up with an image similarity detection model which will identify the similarities between two images. Features of the one image are compared with the other to find how similar they are. The scale-invariant feature transform (SIFT) algorithm is used to detect similarity between input images and also to calculate the similarity score up to which extent the images are matched. SIFT detects the keypoints and computes its descriptors. We will find the best matches of the descriptors by using FLANN-based algorithm. It takes the descriptor of first image and compares with second image. The accuracy point which we achieved in translational image similarity is 60% and in rotational image similarity is 90%, and feature matching similarity differs depending upon the given inputs.
Car crash detection in videos

Increasing the number of cars and excessive traffic congestion in cities is a major problem in the current time. Statistics show that more and more accidents happen daily, and many of these could be avoided. This article aims to develop a system capable of detecting the possibility of an accident by analyzing a video sequence. In this sense, this paper presents a dataset built on those available on the Internet and a series of video classification models. Based on the experimental results obtained, we show what are the main vulnerabilities of each model. The main contribution is that we tried different architectures on a dataset that contains videos from a first-person perspective of the camera, being more challenging to generalize this behavior, but more useful for the autonomous driving systems.
Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences

Forensically trained facial reviewers are still considered as one of the most accurate approaches for person identification from video records. The human brain can utilize information, not just from a single image, but also from a sequence of images (i.e., videos), and even in the case of low-quality records or a long distance from a camera, it can accurately identify a given person. Unfortunately, in many cases, a single still image is needed. An example of such a case is a police search that is about to be announced in newspapers. This paper introduces a face database obtained from real environment counting in 17,426 sequences of images. The dataset includes persons of various races and ages and also different environments, different lighting conditions or camera device types. This paper also introduces a new multi-frame face super-resolution method and compares this method with the state-of-the-art single-frame and multi-frame super-resolution methods. We prove that the proposed method increases the quality of face images, even in cases of low-resolution low-quality input images, and provides better results than single-frame approaches that are still considered the best in this area. Quality of face images was evaluated using several objective mathematical methods, and also subjective ones, by several volunteers. The source code and the dataset were released and the experiment is fully reproducible.
Toward accurate platform-aware performance modeling for deep neural networks

In this paper, we provide a fine-grain machine learning-based method, PerfNetV2, which improves the accuracy of our previous work for modeling the neural network performance on a variety of GPU accelerators. Given an application, the proposed method can be used to predict the inference time and training time of the convolutional neural networks used in the application, which enables the system developer to optimize the performance by choosing the neural networks and/or incorporating the hardware accelerators to deliver satisfactory results in time. Furthermore, the proposed method is capable of predicting the performance of an unseen or non-existing device, e.g. a new GPU which has a higher operating frequency with less processor cores, but more memory capacity. This allows a system developer to quickly search the hardware design space and/or fine-tune the system configuration. Compared to the previous works, PerfNetV2 delivers more accurate results by modeling detailed host-accelerator interactions in executing the full neural networks and improving the architecture of the machine learning model used in the predictor. Our case studies show that PerfNetV2 yields a mean absolute percentage error within 13.1% on LeNet, AlexNet, and VGG16 on NVIDIA GTX-1080Ti, while the error rate on a previous work published in ICBD 2018 could be as large as 200%.
Persona analytics: Analyzing the stability of online segments and content interests over time using non-negative matrix factorization

Personified big data and rapidly developing data science techniques enable previously unforeseen methodological developments for longitudinal analysis of online audiences. Applying data-driven persona generation on online customer statistics from a real organizational social media channel, we demonstrate how personas can be deployed to understand online customer patterns over time. We conduct 32 monthly rounds of data collection of customer demographics and content consumption patterns on the YouTube channel of a major publishing organization posting thousands of items of content and then algorithmically generate 15 personas monthly. We analyze the data-driven persona for changes monthly, yearly, and lifetime (period). Results show an average 40% change in the personas, and 78% of the personas experience more change than consistency for topic interests. The implications are that organizations frequently publishing online content should employ automatic data collection and periodic persona creation to ensure their customer understanding is current. For this, algorithmic data-driven systems that leverage methods for persona creation are recommended.
RobustFed: A Truth Inference Approach for Robust Federated Learning

Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to collaboratively train a global model under a central server's orchestration while keeping local data private. However, the aggregation step in federated learning is vulnerable to adversarial attacks as the central server cannot enforce clients' behavior. As a result, the performance of the global model and convergence of the training process can be affected under such attacks. To mitigate this vulnerability, existing works have proposed robust aggregation methods such as median based aggregation instead of averaging. While they ensure some robustness against Byzantine attacks, they are still vulnerable to label flipping and Gaussian noise attacks. In this paper, we propose a novel robust aggregation algorithm inspired by the truth inference methods in crowdsourcing by incorporating the clients' reliability into aggregation. We evaluate our solution on three real-world datasets with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning and is resilient to various types of attacks, including noisy data attacks, Byzantine attacks, and label flipping attacks.