Reviewerly/exHarmony huggingface dataset:
Polymedia repertoires of networked individuals

Abstract This article introduces the concept of the polymedia repertoire to explore how social meaning is indexed through the interplay of communicative resources at different levels of expression (from choice of media to individual signs) in digitally mediated interactions. The multi-layered polymedia repertoire highlights how people move fluidly between media platforms, semiotic modes and linguistic resources in the course of their everyday interactions, and enables us to locate digital communications within individuals’ wider practices. The potential of our theoretical contribution is illustrated through analysis of mobile phone messaging between participants in a large multi-sited ethnography of the communicative practices of multilingual migrants working in linguistically diverse UK city neighbourhoods. Our analysis of mobile messaging exchanges in a day-in-the-life of these networked individuals reveals the importance of device attention in shaping interpersonal interactions, as well as the complex ways in which choices at different levels of a polymedia repertoire are structured by social relationships, communicative purpose and (dis)identification processes.
Trustful data trading through monetizing IoT data using BlockChain based review system

Abstract In this article, Internet of Things (IoTs) devices are used for sensing the data through which the device owners earn revenue. Interested users can purchase data from IoT device owners, according to their demands. However, users are not confident about the quality of data they are purchasing. Moreover, the users do not rely on the device owner and are not willing to initiate data trading. Currently, data trading systems have many drawbacks, as they involve a third party, security and reputation mechanisms. Therefore, in this article, IoTs and BlockChain (BC) are integrated to monetize IoT's data and provide trustful data trading. A BC based review system to monetize IoT's data trading is developed through Ethereum smart contracts. The review system encourages the owners to provide authentic data and solves the issues regarding data integrity, fake reviews and conflicts between entities. Reviews and ratings are stored in the BC database for providing a guarantee about the data quality to users. To maintain data integrity, we use an advanced encryption standard (AES)‐256 encryption technique to encrypt data. Moreover, an arbitrator entity is responsible to resolve conflicts between data owner and users. The incentive is provided to the users and arbitrators to increase user participation and honesty. Simulations are performed for the validation of our system. We examine the proposed model using three parameters: gas consumption, mining time and encryption time.
Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

An authorized traffic controller (ATC) has the highest priority for direct road traffic. In some irregular situations, the ATC supersedes other traffic control. Human drivers indigenously understand such situations and tend to follow the ATC; however, an autonomous vehicle (AV) can become confused in such circumstances. Therefore, autonomous driving (AD) crucially requires a human-level understanding of situation-aware traffic gesture recognition. In AVs, vision-based recognition is particularly desirable because of its suitability; however, such recognition systems have various bottlenecks, such as failing to recognize other humans on the road, identifying a variety of ATCs, and gloves in the hands of ATCs. We propose a situation-aware traffic control hand-gesture recognition system, which includes ATC detection and gesture recognition. Three-dimensional (3D) hand model-based gesture recognition is used to mitigate the problem associated with gloves. Our database contains separate training and test videos of approximately 60 min length, captured at a frame rate of 24 frames per second. It has 35,291 different frames that belong to traffic control hand gestures. Our approach correctly recognized traffic control hand gestures; therefore, the proposed system can be considered as an extension of the operational domain of the AV.
EdgeBOL

Supporting Edge AI services is one of the most exciting features of future mobile networks. These services involve the collection and processing of voluminous data streams, right at the network edge, so as to offer real-time and accurate inferences to users. However, their widespread deployment is hampered by the energy cost they induce to the network. To overcome this obstacle, we propose a Bayesian learning framework for jointly configuring the service and the Radio Access Network (RAN), aiming to minimize the total energy consumption while respecting desirable accuracy and latency thresholds. Using a fully-fledged prototype with a software-defined base station (BS) and a GPU-enabled edge server, we profile a state-of-the-art video analytics AI service and identify new performance trade-offs. Accordingly, we tailor the optimization framework to account for the network context, the user needs, and the service metrics. The efficacy of our proposal is verified in a series of experiments and comparisons with neural network-based benchmarks.
User Experience Is All There Is

Abstract Twenty years ago, we published an article in the first issue of the i-com entitled “Usability ist nicht alles” (Burmester et al., 2002), that is, “Usability isn’t everything”. This was certainly a provocative title. For most German researchers and practitioners of Human-Computer Interaction (HCI) usability was all there is and all that was ever needed to guarantee humane technology. Back then, we profoundly disagreed. We were convinced that there is more to the quality of interactive technology than mere effectiveness and efficiency. Now, twenty years later it seems safe to say that we had a point. Let’s take this as an opportunity to take a brief and utterly anecdotal look back, to take stock of the current perspective on designing the (user) experience, as well as to discuss some future opportunities and challenges.
Bhatia-Davis formula in the quantum speed limit

The Bhatia-Davis theorem provides a useful upper bound for the variance in mathematics, and in quantum mechanics, the variance of a Hamiltonian is naturally connected to the quantum speed limit due to the Mandelstam-Tamm bound. Inspired by this connection, we construct a formula, referred to as the Bhatia-Davis formula, for the characterization of the quantum speed limit in the Bloch representation. We first prove that the Bhatia-Davis formula is an upper bound for a recently proposed operational definition of the quantum speed limit, which means it can be used to reveal the closeness between the timescale of certain chosen states to the systematic minimum timescale. In the case of the largest target angle, the Bhatia-Davis formula is proved to be a valid lower bound for the evolution time to reach the target when the energy structure is symmetric. Regarding few-level systems, it is also proved to be a valid lower bound for any state in two-level systems with any target, and for most mixed states with large target angles in equally spaced three-level systems.
Local symmetric directional pattern: A novel descriptor for extracting compact and distinctive features in face recognition

This paper presents a novel approach in feature extraction, Local Symmetric Directional Pattern (LSDP), for face recognition. The LSDP encodes the structure of facial textures based on gradient information in a simple and compact coding approach to produce more distinctive code in less time and memory than existing methods. We extract gradient information by convolving the face image with four symmetric compass masks to encode this information using directional numbers, which are related to directional information, and magnitudes of the two prominent edge responses. We also use a hybrid feature vector as a face descriptor obtained by reducing the dimensions of the LSDP feature map and classify them using the sparse representation-based classification (SRC) algorithm. Due to the high discrimination power of the extracted features, the construction of a dictionary based on the hybrid features leads to more sparse representation coefficients. As a result, it improves SRC performance in terms of recognition rate and computational speed. We perform extensive experiments to evaluate and compare the performance of our descriptor with other handcrafted descriptors and the deep learning feature under two different evaluation protocols (different dimensions of feature space and the different number of training samples) on different face databases, which have different variations of illumination, expression, and pose. Experimental results illustrate that our method achieves the highest recognition rate compared to other methods in both evaluation protocols. Especially under challenging conditions where the dimensions of the feature space or the number of training samples are low, LSDP demonstrates excellent performance.
Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing

Vehicles are becoming interconnected and autonomous while collecting, sharing and processing large amounts of personal, and private data. When developing a service that relies on such data, ensuring privacy preserving data sharing and processing is one of the main challenges. Often several entities are involved in these steps and the interested parties are manifold. To ensure data privacy, a variety of different de-identification techniques exist that all exhibit unique peculiarities to be considered. In this paper, we show at the example of a location-based service for weather prediction of an energy grid operator, how the different de-identification techniques can be evaluated. With this, we aim to provide a better understanding of state-of-the-art de-identification techniques and the pitfalls to consider by implementation. Finally, we find that the optimal technique for a specific service depends highly on the scenario specifications and requirements.
Optimal tilt angle for solar photovoltaic modules on pitched rooftops: A case of low latitude equatorial region

In this study, the optimal tilt angle with respect to the module azimuth angle was investigated for the case of low latitude equatorial region. A replicable methodology for establishing the appropriate rooftop support structure adjustments required to attain the maximum possible solar irradiance on pitched rooftops was proposed. By using the particle swarm optimization technique, the study results revealed that for the case of Uganda, the annual optimal tilt angle and average monthly optimal angle vary in the range 0.0 – 5.1 and 2.1 – 11.2, respectively, for all the latitudes and module azimuth angles. Generally, due to the combined advantages of high total annual solar irradiance received by a module and the practicality of installation adjustment, the annual optimal tilt angle is the best installation option for pitched rooftops for all azimuth angles. A tilt angle equivalent to the rooftop pitch angle resulted in a significant reduction in the total solar irradiance received by the module in comparison to the annual optimal tilt angle. An adjustment in the support structure in the range of 0.1 – 1.2 m is required to maximize the solar irradiance received by a module on the pitched rooftops in Uganda.
Contrastive predictive coding with transformer for video representation learning

This paper presents a novel framework of self-supervised learning for video representation. Inspired by Contrastive Predictive Coding and Self-attention, we make the following contributions: First, we propose the Contrastive Predictive Coding with Transformer (CPCTR) framework for video representation learning in a self-supervised fashion. Second, we introduce the Transformer architecture to CPCTR to capture long-range spatio-temporal dependencies in order to facilitate the learning of "slow features" in video, and we conduct analysis of Transformer in our model to show its effectiveness. Finally, we evaluate our model by first training on the UCF101 dataset with self-supervised learning, and then fine-tuning on downstream video classification tasks. Using RGB only video data, we achieve state-of-the-art self-supervised performance on both UCF101 (Top1 accuracy of 99.3%) and HMDB51 (Top1 accuracy of 82.4%), we show that CPCTR even outperforms fully supervised methods on the two datasets. The code is available at https://github.com/yliu1229/CPCTR.
Human Activity Recognition in Smart Home using Deep Learning Techniques

To understand the human activities and anticipate his intentions Human Activity Recognition(HAR) research is rapidly developing in tandem with the widespread availability of sensors. Various applications like elderly care and health monitoring systems in smart homes use smartphones and wearable devices. This paper proposes an effective HAR framework that uses deep learning methodology like Convolution Neural Networks(CNN), variations of LSTM(Long Short term Memory) and Gated Recurrent Units(GRU) Networks to recognize the activities based on smartphone sensors. The hybrid use of CNN-LSTM eliminates the handcrafted feature engineering and uses spatial and temporal data deep. The experiments are carried on UCI HAR and WISDM data sets, and the comparison results are obtained. The result shows a better 96.83 % and 98.00% for the UCI-HAR and WISDM datasets, respectively.
Efficient Privacy-Preserving Federated Learning With Unreliable Users

Federated learning (FL) has emerged as a powerful technology widely applied in Internet of Things (IoT). Recently, researchers have shown an increased interest in privacy-preserving FL with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . The goal of such works is to achieve private training under ciphertext mode while ensuring that the FL model is mainly derived from the contributions of users with high-quality data. However, the existing work is still in its infancy, and the main challenge faced by many researchers is how to achieve their schemes for meeting the demands of high accuracy and efficiency. To combat that, we propose an efficient privacy-preserving FL (EPPFL) scheme with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . Specifically, we design a novel scheme to mitigate the negative impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> , where the targeted model is guaranteed to be updated with high-quality data. Through iteratively executing our “Excluding Irrelevant Components” and “Weighted Aggregation,” the FL model converges rapidly while taking limited communication and computation overhead. As a result, not only the model accuracy can be optimized, but also the training efficiency can be improved. Meanwhile, we conduct a secure framework based on the threshold Paillier cryptosystem, which can rigorously protect all user-related private information during the training process. Furthermore, the extensive experiments demonstrate our EPPFL with high-level performance in terms of accuracy and efficiency.
Can machine learning bring cardiovascular risk assessment to the next level? A methodological study using FOURIER trial data

Through this proof of concept, we studied the potential added value of machine learning (ML) methods in building cardiovascular risk scores from structured data and the conditions under which they outperform linear statistical models.Relying on extensive cardiovascular clinical data from FOURIER, a randomized clinical trial to test for evolocumab efficacy, we compared linear models, neural networks, random forest, and gradient boosting machines for predicting the risk of major adverse cardiovascular events. To study the relative strengths of each method, we extended the comparison to restricted subsets of the full FOURIER dataset, limiting either the number of available patients or the number of their characteristics. When using all the 428 covariates available in the dataset, ML methods significantly (c-index 0.67, P-value 2e-5) outperformed linear models built from the same variables (c-index 0.62), as well as a reference cardiovascular risk score based on only 10 variables (c-index 0.60). We showed that gradient boosting-the best performing model in our setting-requires fewer patients and significantly outperforms linear models when using large numbers of variables. On the other hand, we illustrate how linear models suffer from being trained on too many variables, thus requiring a more careful prior selection. These ML methods proved to consistently improve risk assessment, to be interpretable despite their complexity and to help identify the minimal set of covariates necessary to achieve top performance.In the field of secondary cardiovascular events prevention, given the increased availability of extensive electronic health records, ML methods could open the door to more powerful tools for patient risk stratification and treatment allocation strategies.
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data

Deep learning promises performant anomaly detection on time-variant datasets, but greatly suffers from low availability of suitable training datasets and frequently changing tasks. Deep transfer learning offers mitigation by letting algorithms built upon previous knowledge from different tasks or locations. In this article, a modular deep learning algorithm for anomaly detection on time series datasets is presented that allows for an easy integration of such transfer learning capabilities. It is thoroughly tested on a dataset from a discrete manufacturing process in order to prove its fundamental adequacy towards deep industrial transfer learning - the transfer of knowledge in industrial applications' special environment.
Similarity Software Requirement Specification (SRS) Elicitation Based on the Requirement Statement Using Text Mining on the MNC Play Inventory Management Application

In the software development stage, there are needs analysis activities carried out through elicitation activities, which will produce requirements specification documents. In this document, there is a discrepancy in the perception of elicitation results between developers and clients regarding software requirements based on two methods, namely: elicitation and the Requirement Statement. There are specific steps to achieve this goal, namely: determining the similarity between the software requirements specifications and the elicitation results, analyzing the text contained in the elicitation results using the text mining method, and testing the validity and reliability of the research results through the use of Gwet's AC1 formula. The results obtained are the document with the highest similarity value is the 6th document, namely "The manager can approve items that the supervisor has approved," and the 8th document, namely "Admin can approve items that supervisors and managers have approved." The resulting similarity figure is 0.927587, which means that the similarity level is very high from the number, which is close to number 1.
Improved Target Detection Algorithm Based on YOLO

In the field of target recognition, YOLO algorithm has performed well. In this paper, we improve the latest YOLO network model by changing the residual units to dense connection in the CSP module and adding channel attention mechanism. The improved network model alleviates the vanishing-gradient problem, enhances feature propagation, encourages feature reuse, and reduces the number of parameters. What’s more, it can adaptively recalibrate the channel information of the feature maps and improve the performance of target detection. Experimental results show that the improved YOLO network model greatly improves the detection accuracy. In addition, it optimizes the problem of missing and mis-detecting targets.
Edge Learning for Low-Latency Video Analytics: Query Scheduling and Resource Allocation

Low-latency and accuracy-guaranteed video analytics is essential to many delay-sensitive camera-based applications. Analyzing video frames on edge nodes in proximity can effectively reduce the response delay compared with cloud-based solutions. However, the computation and bandwidth resources on an edge node are always limited. In this paper, we design a joint video query scheduling and resource allocation problem based on an edge coordinated architecture, in order to properly accommodate real-time video queries on end cameras, the edge nodes, or the cloud. This problem is challenging in that 1) the arrivals of video queries with different resource requirements are unknown in advance and 2) the design space (of both query scheduling and resource allocation) to provision video queries varies over time. Taking the two-fold uncertainty into consideration, we formulate the query provision problem as a mix integer non-linear program which is NP-hard and not solved directly. To deal with the NP-hardness and the absence of future information, the problem is re-formulated as a Markov decision process, which can leverage historical query information to make decisions about scheduling and resource allocation. The transformed problem calls for an online solution that can efficiently adapt to the dynamic design space. Hence, we propose an edge-coordinated reinforcement learning algorithm to continuously learn from the environment, and make decisions for query scheduling and resource allocation to achieve low latency and accurate video analytics. Extensive simulation results demonstrate the advantages of the proposed algorithm in latency and accuracy.
Data money makers: An ethnographic analysis of a global cryptocurrency community

Proposed by an unknown young man using the pseudonym Electra01, Electra appeared in the cryptocurrency markets in 2018. Thanks to its innovative supply mechanism, the money's market capitalization briefly reached 136 billion USD, surpassing Bitcoin in value. Focusing on the empirical case of Electra, a project that was chosen as the "best cryptocurrency community" by the users of world's largest exchange, Binance, this paper ethnographically analyzes the sociological universe of a cryptocurrency community. The research consists of two years of fieldwork, interviews with the core team and the project's anonymous founder, a survey among its community members, and computational analyses of the interaction data of the project's Twitter community of 376,600 handles, as well as their Bitcointalk forum. The paper approaches the socio-technical universe of a cryptocurrency community by examining the devices, networks, and representations that the community actors produce, use, and maintain in data-money making and proposes a way to make visible and examine centers of power in a global community operating a seemingly "decentralized" blockchain.
Traffic Sign Recognition Algorithm Based on Improved YOLOv5s

To resolve the problem that the YOLO(You Only Look Once)v5s algorithm model is difficult to accomplish the traffic sign recognition task in small target detection, this paper proposes a YOLOv5s-MobileNetV2 algorithm, replacing the original backbone network DarkNet-53 of YOLOv5s with MobileNetV2 network for feature extraction, and selecting Adam as the optimizer. TT100K dataset containing information of up to 221 traffic sign categories was selected for training. The testing results show that compared with YOLOv5s algorithm, the algorithm in this paper reduces the parameter amount by 65.6% and the computation amount by 59.1% based on the mAP(mean Average Precision) improvement of 0.129.