Reviewerly/exHarmony huggingface dataset:
Impact of Open Innovation on Firm Performance: A Literature Review

This study investigates how the open innovation paradigm influences firm performance by synthesizing the existing literature on open innovation and firm performance. This study provides a better understanding of the concept of open innovation based on the current literature. The selection criteria were executed by the PRISMA article selection process linking open innovation and firm performance, published in peer-reviewed journals from 2015 to June 2021. The search string was tailored by Ebsco host, Elsevier, Emerald Insight and Sage publishers considering the inclusion and exclusion criteria. The descriptive method was used to analyze the selected articles. Numerous studies investigated that open innovation positively impacts firm performance, and few researchers demonstrated that open innovation has a U-shaped relationship with firm performance. Some studies discovered negative and mixed results on open innovation and firm performance. Due to the contradictory findings, more investigations on open innovation and firm performance are needed and this review produces knowledge for both practitioners and policymakers to implement their roles effectively. Especially these findings are significant for future researchers to identify the key areas that need to be investigated in the field of open innovation.
A Touch of Realities: Car-Interior-Based Haptic Interaction Supports In-Car VR Recovery from Interruptions

Real-world interruptions will challenge virtual reality (VR) users in future everyday transport. For example, while passengers are immersed at a virtual beach, an incoming phone call might interrupt their presence and relaxation. We investigated how to help users recover from such interruptions by exploring haptic and visual cues that help them recall their prior presence in VR. We approached this by developing a passive haptic display for rear-seat passengers using an interactive armrest. In a lab study (N=30), participants played with virtual sand to relax, feeling the changes in the real armrest and seeing them on the virtual beach. We compared this multi-sensory experience to the single modalities (just visuals or just haptics). The results showed that the multi-modal experience lowered awareness of the armrest more and fostered a feeling of connectedness to the virtual world after real-world interruptions. We propose using car-interior-based haptic displays to support in-car VR recovery from interruptions.
Designing Gaze-Aware Attention Feedback for Learning in Mixed Reality

Mixed Reality (MR) has demonstrated its potential in the application field of education. In particular, in contrast to traditional learning, students using MR get the possibility of learning and exploring the content in a self-directed way. Meanwhile, research in learning technology has revealed the significance of supporting learning activities with feedback. Since such feedback is often missing in MR-based learning environments, we propose a solution of using eye-tracking in MR to provide gaze-aware attention feedback to students and evaluate it with potential users in a preliminary user study.
A Speech-Based AI for Political Participation

This study presents a first experimental approach for the use of intelligent virtual assistants (IVA) to support political participation. In order to involve as many citizens as possible in participatory political processes, such as the search for a repository site for high-level radioactive waste, IVAs could offer a possibility to convey information in an interactive way and to arouse interest in such complex topics. However, the question arises whether an IVA can adequately convey such a topic and ensure appropriate usability despite many complex dialogues with the user. The explorative study presents the results with a prototypically implemented Amazon Alexa Skill. Compared to a website that addresses the same questions as the Skill, a slightly poorer usability was found. Based on this first study, various questions arose that need to be investigated in future studies. These include questions related to the trustworthiness of such applications and challenges related to the auditive representations of different political opinions.
“I Want My Control Room To Be…”: On the Aesthetics of Interaction in a Safety-Critical Working Environment

Control rooms are safety-relevant working environments characterized by complex IT infrastructure. With regard to the interaction of operators with control room systems, usability has been the major criteria for decades. However, there is increasing discussion about the extent to which the concept of user experience (UX) also plays a role in such safety-critical contexts. What is still largely missing is the application of concrete UX-specific methods in the context of control rooms. This paper explains how and with what results 9 operators used an interaction vocabulary focusing on pragmatic and hedonic qualities to complete the sentence "I want my control room to be…". Results first suggest that pragmatic, i.e., usability-oriented, attributions are of greater importance to operators. However, especially the more UX-specific terms of the interaction vocabulary, which were initially not found to be so relevant, yielded many valuable hints and inspiration for the future design of control room workplaces. By reflecting on the process of discussing the aesthetics of interactions in such a safety-critical working environment, recommendations are provided for considering UX in safety.
Emergency Alerting Through Smartphone Apps

In Germany alone, there are more than one million active members of fire departments, relief organizations and disaster control [1]. In case of an emergency, alerting apps are being used more and more in both professional and lay contexts. Fire departments, in particular, supplement alerts of their firefighters with alerting apps. These apps send the same alarm cue to the smartphones simultaneously with the (digital) radio message receivers of the first responders. To date, an overview of the functions of such apps and their respective importance is missing. Our research fills this research gap by summarizing functions of alerting apps in an overview manner. In addition, first responders (N = 287) as users of alerting apps rated and ranked the functions in terms of their importance, the app usability and were openly asked about advantages and disadvantages of such apps. The resulting ranking enables the selection of suitable alerting apps. However, both quantitative data and responses to open questions show clear deficits mentioned by the users in the area of usability and technical requirements. This is particularly critical, since every second counts in the alerting of first responders.
MultiKnob — A Knob for Multiplexing Rotation Inputs by Multitouch-based Grasp Recognition

Rotary knobs are used on many devices. However, while we enrich the functionalities of smart devices, controlling such functionalities by using the rotation as sole input appears to be rather limited. Therefore, we created a physical rotary controller which enriches the input capabilities of a rotary knob, called MultiKnob. It takes the count of applied fingers as an input parameter for multiplexing rotation. The paper discusses three iterations of our prototyping: Beginning with an attempt to extend an existing rotary controller using conductive paint, to the development of several conductive 3D printed prototypes for use on a smartphone display, to a cylinder sensing multi-touch based on infrared light. We discuss possible applications and limitations of a knob which allows to recognize a user’s grasp.
Digital Security Story Cards for Women with a Refugee and Migrant Background

Our work focuses on the design process of a prototype of "story cards" to address, learn and reflect on digital security topics through dialogue and storytelling, for women with a refugee and migrant background. We describe crucial aspects behind the design process of the prototype, including related work, the process behind the identification of needs and challenges around digital security and ICT usage of our target group, as well as our findings, limitations, reflections, and opportunities for future work. With this contribution, we want to shed light on the necessity of digital security learning materials that are accessible and adequate to the needs of individuals and groups, particularly women, with a refugee and migrant background.
Demonstration of an Infrared Pen as an Input Device for Projected Augmented Reality Tabletops

Interactive tabletops do not only offer a large surface for collaborative interaction. They also offer quick access to digital tools directly at the table - where a large number of everyday activities take place. Tabletops with an embedded display are generally less flexible and more fragile than ordinary massive tabletops. Physical objects on the tabletop occlude the digital content. In contrast, top-down-projected interfaces using an overhead projector-camera system allow for augmenting arbitrary tables and objects lying on them. However, detecting pointing input only via a camera image captured from above requires robustly recognizing whether a finger or pen touches the tabletop or whether it hovers slightly above it. In this demonstration, we showcase a solution for reliably tracking a pen on arbitrary tabletop surfaces. The pen emits infrared light via a tip made of optical fiber. A camera captures position and shape of the light point on the surface. Our open-source tracking algorithm combines heuristics and a neural network to distinguish between drawing and hovering. This system can be reliably used for drawing and writing on tabletops. However, occlusion by users’ hands can deteriorate tracking of the pen.
Hey, Siri®! Ok, Google®! Does Talking to Voice Assistants Enhance Emotional Aspects of Mobile Phone User Experience?

This German online study (N = 665) examines the influence of voice control usage on emotional aspects of mobile phone user experience. Frequent use of voice control is associated with both positive and, with limitations, also negative emotions towards the mobile phone. The study only partially confirms Herzberg’s two-factor theory when transferred to user experience. A more frequent use of voice control primarily affects positive emotions, but the combination of both emotion types can make a difference.
Adopting Just Another Digital Assistant or Establishing Social Interactions with a New Friend?

Social interactions have been exclusive to human-human interactions, until recently. However, modern technologies with adaptive and intelligent features such as smart speakers imitate human social characteristics and appear to be allegedly social interaction partners. Research going beyond the analysis of mere usage and conceptualizing devices as interaction partners is rare, also because studying the establishment of social interactions would require analyses over time. The present paper addresses this research desideratum and introduces a concept of a longitudinal study which involves the collection of different data types from different sources (online questionnaire, providers’ log files, interview) and interdisciplinary data analytical methods (social science, data science). Its cross-sectional and longitudinal research questions ask for the adoption and the usage of the usage of smart speakers, for the establishment of social interactions with the device. In sum, this study contributes to the research area by presenting a conceptual research model, which provides a framework for the systematic long-term analysis of both the adoption of technology over time as well as the establishment of potential social interactions.
High capacity reversible data hiding for encrypted images based on full bit-plane compression

To achieve a high embedding capacity (EC) without any distortion in the directly decrypted result, a reversible data hiding in encrypted images (RDH-EI) scheme based on full bit-plane compression (FBPC) is proposed. FBPC is designed to vacate as much room as possible before image encryption. To enrich the adjacent redundancy within the most significant bit (MSB) planes, we use flip prediction on the first MSB and then replace the other MSB planes with the XOR result between that plane and its higher level one successively. Hilbert curve scanning is introduced to reduce the dimensionality of the planes to obtain eight highly redundant bit sequences. Huffman coding is then utilizing to compress the bit sequences. After FBPC, a compressed image is obtained. Stream cipher with self-feedback is adapted to encrypt the image. Additional data could be embedded into the reserved space of the encrypted domain. Compared with existing RDH-EI algorithms, the proposed scheme could have a higher EC with no distortion in the directly decrypted image due to the high compression performance of FBPC. Experimental results on the BOWS-2 and BOSSbass datasets demonstrate that the average EC can reach 3.56 and 3.71 bits per pixel, respectively.
Distillation of human–object interaction contexts for action recognition

Abstract Modeling spatial‐temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus on learning overall visual cues of a scene but disregard a holistic view of human–object relationships and interactions, that is, how a human interacts with respect to short‐term task for completion and long‐term goal. We therefore argue to improve human action recognition by exploiting both the local and global contexts of human–object interactions (HOIs). In this paper, we propose the Global‐Local Interaction Distillation Network (GLIDN), learning human and object interactions through space and time via knowledge distillation for holistic HOI understanding. GLIDN encodes humans and objects into graph nodes and learns local and global relations via graph attention network. The local context graphs learn the relation between humans and objects at a frame level by capturing their co‐occurrence at a specific time step. The global relation graph is constructed based on the video‐level of human and object interactions, identifying their long‐term relations throughout a video sequence. We also investigate how knowledge from these graphs can be distilled to their counterparts for improving HOI recognition. Finally, we evaluate our model by conducting comprehensive experiments on two datasets including Charades and CAD‐120. Our method outperforms the baselines and counterpart approaches.
<scp>AccHashtag</scp> : Accelerated Hashing for Detecting Fault-Injection Attacks on Embedded Neural Networks

We propose AccHashtag , the first framework for high-accuracy detection of fault-injection attacks on Deep Neural Networks (DNNs) with provable bounds on detection performance. Recent literature in fault-injection attacks shows the severe DNN accuracy degradation caused by bit flips. In this scenario, the attacker changes a few DNN weight bits during execution by injecting faults to the dynamic random-access memory (DRAM). To detect bit flips, AccHashtag extracts a unique signature from the benign DNN prior to deployment. The signature is used to validate the model’s integrity and verify the inference output on the fly. We propose a novel sensitivity analysis that identifies the most vulnerable DNN layers to the fault-injection attack. The DNN signature is constructed by encoding the weights in vulnerable layers using a low-collision hash function. During DNN inference, new hashes are extracted from the target layers and compared against the ground-truth signatures. AccHashtag incorporates a lightweight methodology that allows for real-time fault detection on embedded platforms. We devise a specialized compute core for AccHashtag on field-programmable gate arrays (FPGAs) to facilitate online hash generation in parallel to DNN execution. Extensive evaluations with the state-of-the-art bit-flip attack on various DNNs demonstrate the competitive advantage of AccHashtag in terms of both attack detection and execution overhead.
Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents

Abstract Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.
An End-to-End Mutually Interactive Emotion–Cause Pair Extractor via Soft Sharing

Emotion–cause pair extraction (ECPE), i.e., extracting pairs of emotions and corresponding causes from text, has recently attracted a lot of research interest. However, current ECPE models face two problems: (1) The common two-stage pipeline causes the error to be accumulated. (2) Ignoring the mutual connection between the extraction and pairing of emotion and cause limits the performance. In this paper, we propose a novel end-to-end mutually interactive emotion–cause pair extractor (Emiece) that is able to effectively extract emotion–cause pairs from all potential clause pairs. Specifically, we design two soft-shared clause-level encoders in an end-to-end deep model to measure the weighted probability of being a potential emotion–cause pair. Experiments on standard ECPE datasets show that Emiece achieves drastic improvements over the original two-step ECPE model and other end-to-end models in the extraction of major emotional cause pairs. The effectiveness of soft sharing and the applicability of the Emiece framework are further demonstrated by ablation experiments.
Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery

With the advancement of an intellectual and numerical society, the coal mining industry has also begun to change to intelligence. As an important aspect of intelligent coal mine construction, coal mine communication has put forward more stringent standards for communication quality. For the complex communication environment in mines, the transmission of communication signals is always damaged by various noises and interferences, resulting in serious distortion of the communication signals received at the receiving end. Therefore, the use of traditional receivers for information recovery has the problem of high bit error rate (BER), which cannot meet the standard of intelligent coal mine construction. Based on this, the aim of this research is to combine convolutional neural networks (CNN) and multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) communication systems to design an intelligent receiver model for complex mine communication systems. At the receiver side, CNNs are used to take the place of all the information processing processes. First, features are extracted from the received IQ signal by the convolutional neural network, and then the original information bit is recovered using a multi-label classifier to finally realize end-to-end information restoration. The experimental results show that the intelligent receiver model designed in this research has more accurate information recovery capability in the complex mine channel environment compared with the traditional receiver. In addition, they also verify that the intelligent receiver can still recover information effectively when the traditional receiver cannot recover information properly in the case of partial loss of received data.
A Scalable Recommendation Engine for New Users and Items

In many digital contexts such as online news and e-tailing with many new users and items, recommendation systems face several challenges: i) how to make initial recommendations to users with little or no response history (i.e., cold-start problem), ii) how to learn user preferences on items (test and learn), and iii) how to scale across many users and items with myriad demographics and attributes. While many recommendation systems accommodate aspects of these challenges, few if any address all. This paper introduces a Collaborative Filtering (CF) Multi-armed Bandit (B) with Attributes (A) recommendation system (CFB-A) to jointly accommodate all of these considerations. Empirical applications including an offline test on MovieLens data, synthetic data simulations, and an online grocery experiment indicate the CFB-A leads to substantial improvement on cumulative average rewards (e.g., total money or time spent, clicks, purchased quantities, average ratings, etc.) relative to the most powerful extant baseline methods.
A QUBO formulation for the Tree Containment problem

Phylogenetic (evolutionary) trees and networks are leaf-labeled graphs that are widely used to represent the evolutionary relationships between entities such as species, languages, cancer cells, and viruses. To reconstruct and analyze phylogenetic networks, the problem of deciding whether or not a given rooted phylogenetic network embeds a given rooted phylogenetic tree is of recurring interest. This problem, formally know as Tree Containment, is NP-complete in general and polynomial-time solvable for certain classes of phylogenetic networks. In this paper, we connect ideas from quantum computing and phylogenetics to present an efficient Quadratic Unconstrained Binary Optimization formulation for Tree Containment in the general setting. For an instance (N,T) of Tree Containment, where N is a phylogenetic network with nN vertices and T is a phylogenetic tree with nT vertices, the number of logical qubits that are required for our formulation is O(nNnT).