Reviewerly/exHarmony huggingface dataset:
A hybrid recommender system for patron driven library acquisition and weeding

Nowadays the explosion of information sources has shaped how library users access information and provide feedback on their preferences. Therefore, faced with this explosion and the blossoming of digital libraries, modern libraries must take up the challenge of meeting the needs of their users and considering their opinions and preferences in order to offer them adequate resources and henceforth eliminate those that are unsatisfactory. Almost all library recommender systems aim to provide users with items of interest to them. However, despite its definite interest, the staff-oriented adaptation and application of this revolutionary technique to the collection development process is still in an embryonic stage. We propose a patron-driven hybrid library recommender system that uses machine learning techniques to recommend and assist in the acquisition and weeding decision-making operations by extracting and analyzing users’ opinions and ratings. The recommender system is applied and validated in a real national library case using Amazon’s digital library and the library’s catalog as a data source.
AI as Co-workers: An Explorative Research on Technology Acceptance Based on the Revised Bloom Taxonomy

Artificial intelligence and technology acceptance are both extensively discussed in academic research with various scholars contributing to these areas. This paper outlines an approach to link the two concepts through the analysis of competences accredited to artificial intelligence from a laymen perspective. An interview guideline regarding the acceptance of artificial intelligence was developed based on four science fiction snippets. Each snippet representing one competence level according to Bloom’s taxonomy and different domains of artificial intelligence. To develop our propositions a series of experiments were undertaken. In each experiment four snippets were viewed individually by fourteen participants. The collected data were qualitatively evaluated using grounded theory. The results suggest that existing technology acceptance models should be supplemented with additional variables covering the visibility of, the plausibility of the algorithms applied, and the emotional experience with an artificial intelligence system. A preliminary framework in form of prepositions for further research is presented. We conclude that the acceptance of the daily practice for employees using or co-working with artificial intelligence systems can be increased with the aid of a multi-tier traffic light approach, visualizing the core elements of the artificial intelligence system in place.
DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices

We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.
Learning Both Dynamic-Shared and Dynamic-Specific Patterns for Chaotic Time-Series Prediction

In the real world, multivariate time series from the dynamical system are correlated with deterministic relationships. Analyzing them dividedly instead of utilizing the shared-pattern of the dynamical system is time consuming and cumbersome. Multitask learning (MTL) is an effective inductive bias method to utilize latent shared features and discover the structural relationships from related tasks. Base on this concept, we propose a novel MTL model for multivariate chaotic time-series prediction, which could learn both dynamic-shared and dynamic-specific patterns. We implement the dynamic analysis of multiple time series through a special network structure design. The model could disentangle the complex relationships among multivariate chaotic time series and derive the common evolutionary trend of the multivariate chaotic dynamical system by inductive bias. We also develop an efficient Crank-Nicolson-like curvilinear update algorithm based on the alternating direction method of multipliers (ADMM) for the nonconvex nonsmooth Stiefel optimization problem. Simulation results and analysis demonstrate the effectiveness on dynamic-shared pattern discovery and prediction performance.
Hierarchical Brain Parcellation with Uncertainty

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are 'flat'. We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree. We further show how this method can be used to model uncertainty separately for every branch in this label tree. Our method exceeds the performance of flat uncertainty methods, whilst also providing decomposed uncertainty estimates that enable us to obtain self-consistent parcellations and uncertainty maps at any level of the label hierarchy. We demonstrate a simple way these decision-specific uncertainty maps may be used to provided uncertainty-thresholded tissue maps at any level of the label tree.
What Do You Think About Your Company’s Leaks? A Survey on End-Users Perception Toward Data Leakage Mechanisms

Data leakage can lead to severe issues for a company, including financial loss, damage of goodwill, reputation, lawsuits and loss of future sales. To prevent these problems, a company can use other mechanisms on top of traditional Access Control. These mechanisms include for instance Data Leak Prevention or Information Rights Management and can be referred as Transmission Control. However, such solutions can lack usability and can be intrusive for end-users employees. To have a better understanding of the perception and usage of such mechanisms within business infrastructures, we have conducted in this article an online survey on 150 employees. These employees come from different companies of different sizes and sectors of activity. The results show that whatever the size of the company or its sector of activity, security mechanisms such as access control and transmission control can be considered as quite intrusive and blocking for employees. Moreover, our survey also shows interesting results regarding more acceptable and user-friendly anti-data leakage mechanisms that could be used within companies.
Improved fault analysis on SIMECK ciphers

The advances of Internet of Things (IoT) have had a fundamental impact and influence on sharping our rich living experiences. However, since IoT devices are usually resource-constrained, lightweight block ciphers have played a major role in serving as a building block for secure IoT protocols. In CHES 2015, SIMECK, a family of block ciphers, was designed for resource-constrained IoT devices. Since its publication, there have been many analyses on its security. In this paper, under the one bit-flip model, we propose a new efficient fault analysis attack on SIMECK ciphers. Compared to those previously reported attacks, our attack can recover the full master key by injecting faults into only a single round of all SIMECK family members. This property is crucial, as it is infeasible for an attacker to inject faults into different rounds of a SIMECK implementation on IoT devices in the real world. Specifically, our attack is characterized by exercising a deep analysis of differential trail between the correct and faulty immediate ciphertexts. Extensive simulation evaluations are conducted, and the results demonstrate the effectiveness and correctness of our proposed attack.
Correction: Huang, E.J., and Onnela, J.P. Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data. Sensors 2020, 20(13), 3706

The authors wish to make the following corrections to this paper [...].
The Changing World and the Adapting Machine: How Digital Transformation Changes Requirements Engineering in the Embedded and Cyberphysical Systems Industry

Digitalization changes products and their development. In this article, we investigate embedded and cyberphysical systems using the core technologies of the digital transformation and sketch out the skills that the next generation of requirements engineers must possess.
Portals With a Twist: Cable Twist-Free Natural Walking in Room-Scaled Virtual Reality

To provide naturally walking in small virtual reality (VR) tracking spaces while preventing cables of head-mounted displays (HMDs) getting twisted, we developed Portals With A Twist (PorTwist), a redirected walking method using a portal metaphor. We compared PorTwist with a teleportation method in a 2m × 2.7m tracking space in a within-design user study (N = 34). PorTwist resulted in significantly longer natural walking distances and could prevent HMD cables from getting twisted while providing comparable levels of perceived presence and simulator sickness as teleportation. We further identified potential to improve usability in the future.
Investigating the use of digital artifacts in a community project of sustainable food practices: ‘My chili blossoms’

Research on food practices has become more common among scholars of HCI in recent years. Human-Food-Interaction (HFI) looks into the interplay of humans, food and technology. HFI, even so, has paid relatively little attention to the more collective elements of food practice, including social bonding [1]. The modest project we describe below aimed to say something about the use of digital artifacts to support community engagement for sustainable food practices. We participated, as action researchers (see [2]) in a grassroots movement that instigated a project around learning about food growing, using digital means to bring interested people together during times of physical distancing: In the project Vegetables seek a home, people from various backgrounds 'adopted' a chili-plant, they are invited to share what they like in a Telegram-Group, and to get learning-modules via a mailing-list. Through an analysis of the communal effort to actualize the project (video-calls, Telegram, wechange.de) and the content of the Telegram-Group for the chili-plant adopting parents and experts, we suggest some design implications for grassroots communities and sustainable food practice. In future research we intend an iterative design to support the community and its project, utilizing Holmgren's 12 principles of permaculture design.
A New Visual Cryptography-Based QR Code System for Medication Administration

In health care, medication errors can result in serious health risks to patients, and hospitals require a secure medication administration system to prevent such errors. This paper therefore proposes a secure medication administration method based on threshold sharing technology. When a patient visits a doctor and the doctor prescribes n medications, a photo and the personal information of the patient are encoded into n QR code transparencies that can be decoded by common QR code scanners available for smartphones. The prescription and n QR code transparencies are then stored in a hospital’s medication administration system. When the patient receives their medicine, they can scan these n QR code transparencies using a smartphone to ensure that they have all the medicines prescribed by the doctor; this function is accessible even if the patient’s phone does not have Internet access. The main purpose of the proposed system is to prevent hospitals from giving medicine to the wrong patient, or giving less than the prescribed dosage of medicine to the patient. The focus is not on the internal medicine packaging process in hospitals but on reducing the probability of counter staff giving medicine to the wrong patient. This function is of considerable importance to non-English-speaking people who are not used to reading medicine names in English.
LDflex: A Read/Write Linked Data Abstraction for Front-End Web Developers

Many Web developers nowadays are trained to build applications with a user-facing browser front-end that obtains predictable data structures from a single, well-known back-end. Linked Data invalidates such assumptions, since data can combine several ontologies and span multiple servers with different . Front-end developers, who specialize in creating end-user experiences rather than back-ends, thus need an abstraction layer to the Web of Data that integrates with existing frameworks. We have developed , a domain-specific language that exposes common Linked Data access patterns as reusable JavaScript expressions. In this article, we describe the design and embedding of the language, and discuss its daily usage within two companies. eliminates a dedicated data layer for common and straightforward data access patterns, without striving to be a replacement for more complex cases. The use cases indicate that designing a Linked Data developer experience—analogous to a user experience— is crucial for adoption by the target group, who in turn create Linked Data apps for end users. Crucially, simple abstractions require research to hide the underlying complexity.
Block-Based Steganography Method Using Optimal Selection to Reach High Efficiency and Capacity for Palette Images

The primary goal of steganographic methods is to develop statically undetectable methods with high steganographic capacity. The embedding efficiency is one kind of measure for undetectability. Block-based steganography methods have been proposed for achieving higher embedding efficiency under limited embedding capacity. However, in these methods, some blocks with larger embedding distortions are skipped, and a location map is usually incorporated into these methods to record the embedding status of each block. This reduces the embedding capacity for secret messages. In this study, we proposed a block-based steganography method without a location map for palette images. In this method, multiple secret bits can be embedded in a block by modifying at most one pixel with minimal embedding distortion; this enables each block to be used for data embedding; thus, our method provides higher embedding capacity. Furthermore, under the same capacity, the estimated and experimental embedding efficiencies of the proposed method are compared with those of Imaizumi et al. and Aryal et al.’s methods; the comparisons indicate that the proposed method has higher embedding efficiency than Imaizumi et al. and Aryal et al.’s methods.
Guidelines for enhancing data locality in selected machine learning algorithms

To deal with the complexity of the new bigger and more complex generation of data, machine learning (ML) techniques are probably the first and foremost used. For ML algorithms to produce results in a reasonable amount of time, they need to be implemented efficiently. In this paper, we analyze one of the means to increase the performances of machine learning algorithms which is exploiting data locality. Data locality and access patterns are often at the heart of performance issues in computing systems due to the use of certain hardware techniques to improve performance. Altering the access patterns to increase locality can dramatically increase performance of a given algorithm. Besides, repeated data access can be seen as redundancy in data movement. Similarly, there can also be redundancy in the repetition of calculations. This work also identifies some of the opportunities for avoiding these redundancies by directly reusing computation results. We start by motivating why and how a more efficient implementation can be achieved by exploiting reuse in the memory hierarchy of modern instruction set processors. Next we document the possibilities of such reuse in some selected machine learning algorithms.
Application of Image-Based Visual Servoing on Autonomous Drones

This work presents an Image-Based Visual Servoing (IBVS) technique for an autonomous drone to track and follow an object in 3D space using Image-Based Visual Servoing. Unlike implementing tracking mechanism using GPS positions, Radars or other high-level position determining devices, we present our approach in which we use an onboard camera that captures real-time video stream to processes information such that a given target in 3D space is tracked. We perform IBVS during the flight. A technique to use the video stream and locate the target is presented in this paper. Here we also discuss the mathematical model and system architecture of this technique on an autonomous drone. The advantage of this approach is that the tracking by drones can be made completely autonomous even in GPS-denied environments. We then walk through the applications and future scope of this methodology.
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing

In Machine Learning, White Box Adversarial Attacks rely on knowing underlying knowledge about the model attributes. This works focuses on discovering to distrinct pieces of model information: the underlying architecture and primary training dataset. With the process in this paper, a structured set of input probes and the output of the model become the training data for a deep classifier. Two subdomains in Machine Learning are explored - image based classifiers and text transformers with GPT-2. With image classification, the focus is on exploring commonly deployed architectures and datasets available in popular public libraries. Using a single transformer architecture with multiple levels of parameters, text generation is explored by fine tuning off different datasets. Each dataset explored in image and text are distinguishable from one another. Diversity in text transformer outputs implies further research is needed to successfully classify architecture attribution in text domain.
Exploring and Exploiting Conditioning of Reinforcement Learning Agents

The outcome of Jacobian singular values regularization was studied for supervised learning problems. In supervised learning settings for linear and nonlinear networks, Jacobian regularization allows for faster learning. It also was shown that Jacobian conditioning regularization can help to avoid the “mode-collapse” problem in Generative Adversarial Networks. In this paper, we try to answer the following question: Can information about policy network Jacobian conditioning help to shape a more stable and general policy of reinforcement learning agents? To answer this question, we conduct a study of Jacobian conditioning behavior during policy optimization. We analyze the behavior of the agent conditioning on different policies under the different sets of hyperparameters and study a correspondence between the conditioning and the ratio of achieved rewards. Based on these observations, we propose a conditioning regularization technique. We apply it to Trust Region Policy Optimization and Proximal Policy Optimization (PPO) algorithms and compare their performance on 8 continuous control tasks. Models with the proposed regularization outperformed other models on most of the tasks. Also, we showed that the regularization improves the agent’s generalization by comparing the PPO performance on CoinRun environments. Also, we propose an algorithm that uses the condition number of the agent to form a robust policy, which we call Jacobian Policy Optimization (JPO). It directly estimates the condition number of an agent’s Jacobian and changes the policy trend. We compare it with PPO on several continuous control tasks in PyBullet environments and the proposed algorithm provides a more stable and efficient reward growth on a range of agents.
Optimal Design of Deep Residual Network Based on Image Classification of Fashion-MNIST Dataset

Abstract With the increasing number of layers of deep neural network, the performance of the model tends to be saturated gradually. As a very deep network architecture, the deep residual network shows beneficial characteristics in precision and convergence. This paper deeply studied the essence of deep residual network and intended to design an optimal model for classification in Fashion-MNIST dataset. Due to the significant effects of the hyper-parameters in neural network models, the method of designing model structure and optimizing training process were investigated. Next, the performances of the training models under different conditions were compared, and finally an optimal training model whose accuracy was up to 96.21% was obtained. The experimental result shows that the performance of the model can be improved by partly widening the network and selecting more advanced training process.
Impact of VR Settings on Immersiveness in VR-Based Construction Skills Training: Case Study of Scaffolding Work

In response to the issue of acute skills shortage in many construction trades, increasing attention has recently been paid to the application of virtual reality (VR) technologies to construction skills training. Notably, the construction works involved with high levels of safety risks, such as scaffolding, have been suggested as the potential area where VR-based training can be particularly useful. However, research efforts to understand the impact the conditions of a VR environment have on the trainee’s sense of presence have been scarce. The sense of presence in VR would be essential in the context of training because the effectiveness of a VR-based training can be highly influenced by the level of similarity of the user’s subjective experience in the VR environment. Against this background, this paper aims to investigate whether or not the VR settings used to augment the sense of presence, such as the use of scaffold walking board (“a tangible VR”) and construction site noise sound effect, affect the feeling of being in a real construction site (i.e., sense of presence) or side-effects such as VR-sickness during the VR experience. As an instrument for this research, a prototype of a VR-based scaffolding work training program was developed and used to measure the differences in VR-experience between two different VR settings, one with the immersion-enhancing factors and the other without them. Additionally, self-report surveys were used to measure the psychological aspects of trainees before and after the VR training. The results indicate that immersion-enhancing factors have a significant impact on the users’ experience in VR and their psychological responses.