Reviewerly/exHarmony huggingface dataset:
The Effects of System Initiative during Conversational Collaborative Search

Our research in this paper lies at the intersection of collaborative and conversational search. We report on a Wizard of Oz lab study in which 27 pairs of participants collaborated on search tasks over the Slack messaging platform. To complete tasks, pairs of collaborators interacted with a so-called searchbot with conversational capabilities. The role of the searchbot was played by a reference librarian. It is widely accepted that conversational search systems should be able to engage in mixed-initiative interaction ---take and relinquish control of a multi-agent conversation as appropriate. Research in discourse analysis differentiates between dialog- and task-level initiative. Taking dialog-level initiative involves leading a conversation for the sole purpose of establishing mutual belief between agents. Conversely, taking task-level initiative involves leading a conversation with the intent to influence the goals of the other agent(s). Participants in our study experienced three searchbot conditions, which varied based on the level of initiative the human searchbot was able to take: (1) no initiative, (2) only dialog-level initiative, and (3) both dialog- and task-level initiative. We investigate the effects of the searchbot condition on six different types of outcomes: (RQ1) perceptions of the searchbot's utility, (RQ2) perceptions of workload, (RQ3) perceptions of the collaboration, (RQ4) patterns of communication and collaboration, and perceived (RQ5) benefits and (RQ6) challenges from engaging with the searchbot.
XR Art and Culture: Successful Collaborations in Interdisciplinary Development Processes

Abstract In this paper we present different approaches for interdisciplinary collaborations between art and culture professionals resp. cultural institutions and the interdisciplinary XR experts of our research group. We discuss lessons learned and best practices devised from multiple years of experience in interdisciplinary collaborations. Different project settings need different solutions: With our contribution, we hope to show exemplary ways for successful interdisciplinary collaborations at the intersection of culture and computer science – and to show the potential of such projects for the development of new ways and tools of collaboration.
An Adaptive Gradient Method with Energy and Momentum

We introduce a novel algorithm for gradient-based optimization of stochastic objective functions. The method may be seen as a variant of SGD with momentum equipped with an adaptive learning rate automatically adjusted by an 'energy' variable. The method is simple to implement, computationally efficient, and well suited for large-scale machine learning problems. The method exhibits unconditional energy stability for any size of the base learning rate. We provide a regret bound on the convergence rate under the online convex optimization framework. We also establish the energy-dependent convergence rate of the algorithm to a stationary point in the stochastic non-convex setting. In addition, a sufficient condition is provided to guarantee a positive lower threshold for the energy variable. Our experiments demonstrate that the algorithm converges fast while generalizing better than or as well as SGD with momentum in training deep neural networks, and compares also favorably to Adam.
Evaluating transfer learning approach for detecting Arabic anti-refugee/migrant speech on social media

Purpose The present study was designed to investigate eight research questions that are related to the analysis and the detection of dialectal Arabic hate speech that targeted African refugees and illegal migrants on the YouTube Algerian space. Design/methodology/approach The transfer learning approach which recently presents the state-of-the-art approach in natural language processing tasks has been exploited to classify and detect hate speech in Algerian dialectal Arabic. Besides, a descriptive analysis has been conducted to answer the analytical research questions that aim at measuring and evaluating the presence of the anti-refugee/migrant discourse on the YouTube social platform. Findings Data analysis revealed that there has been a gradual modest increase in the number of anti-refugee/migrant hateful comments on YouTube since 2014, a sharp rise in 2017 and a sharp decline in later years until 2021. Furthermore, our findings stemming from classifying hate content using multilingual and monolingual pre-trained language transformers demonstrate a good performance of the AraBERT monolingual transformer in comparison with the monodialectal transformer DziriBERT and the cross-lingual transformers mBERT and XLM-R. Originality/value Automatic hate speech detection in languages other than English is quite a challenging task that the literature has tried to address by various approaches of machine learning. Although the recent approach of cross-lingual transfer learning offers a promising solution, tackling this problem in the context of the Arabic language, particularly dialectal Arabic makes it even more challenging. Our results cast a new light on the actual ability of the transfer learning approach to deal with low-resource languages that widely differ from high-resource languages as well as other Latin-based, low-resource languages.
Optimal Pricing Decisions for Dual-Channel Supply Chain: Blockchain Adoption and Consumer Sensitivity

Counterfeiting is common in many industries. For the authenticity of online channel products and to combat counterfeiting, many companies have begun to use blockchain technology to trace product information. This paper investigates a dual-channel supply chain consisting of one manufacturer and one retailer, in which the manufacturer sells its standard products through the retailer and adopts blockchain technology to launch the online channel to sell the traceable products. A Stackelberg game is developed to depict the pricing decision and channel strategy choice for the manufacturer. It shows that both the manufacturer and the retailer can benefit from blockchain adoption. And the longer it takes for product traceability, the lower the price of online traceable products. It is beneficial for the manufacturer when consumers in the market are less concerned about information transparency. For consumer sensitivity, as consumers’ acceptance of direct sales channels increases, the selling prices of online traceable products have risen. Furthermore, as the traceability time-sensitivity coefficient increases, the sales price of the traceable products decreases, and the retail price of offline standard products rises. The conclusion of this paper has a certain guiding role for the dominant manufacturers in choosing whether to apply blockchain technology to improve the demand for traceable products.
Multi-word units (and tokenization more generally): a multi-dimensional and largely information-theoretic approach

It has been argued that most of corpus linguistics involves one of four fundamental methods: frequency lists, dispersion, collocation, and concordancing. All these presuppose (if only implicitly) the definition of a unit: the element whose frequency in a corpus, in corpus parts, or around a search word are counted (or quantified in other ways). Usually and with most corpus-processing tools, a unit is an orthographic word. However, it is obvious that this is a simplifying assumption borne out of convenience: clearly, it seems more intuitive to consider because of or in spite of as one unit each rather than two or three. Some work in computational linguistics has developed multi-word unit (MWU) identification algorithms, which typically involve co-occurrence token frequencies and association measures (AMs), but these have not become widespread in corpus-linguistic practice despite the fact that recognizing MWUs like the above will have a profound impact on just about all corpus statistics that involve (simplistic notions of) words/units. In this programmatic proof-of-concept paper, I introduce and exemplify an algorithm to identify MWUs that goes beyond frequency and bidirectional association by also involving several well-known but underutilized dimensions of corpus-linguistic information: frequency: how often does a potential unit (like in_spite_of) occur?; dispersion: how widespread is the use of a potential unit?; association: how strongly attracted are the parts of a potential unit?; entropy: how variable is each slot in a potential unit? The proposed algorithm can use all these dimensions and weight them differently. I will (i) present the algorithm in detail, (ii) exemplify its application to the Brown corpus, (iii) discuss its results on the basis of several kinds of MWUs it returns, and (iv) discuss next analytical steps.
Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

To address the problems of the traditional human motion gesture tracking and recognition methods, such as poor tracking effect, low recognition accuracy, high frame loss rate, and long-time cost, a dynamic human motion gesture tracking and recognition algorithm using multimode deep learning was proposed. Firstly, the collected human motion images are repaired in the three-dimensional (3D) environment, and the multimodal 3D human motion model is reconstructed using the processed images. Secondly, according to the results of model reconstruction, the camera gesture and other parameters of the keyframe are used to construct the target tracking optimization function so as to achieve the purpose of accurate tracking of human motion. Finally, for multimodal human motion gesture learning, a convolutional neural network (CNN) is developed. The trained CNN is utilized to complete dynamic human motion recognition after convolutional and pooling calculations. The results demonstrate that the proposed algorithm is effective in tracking human motion gestures. The average recognition accuracy is 96%, the average frame loss rate is 8.8%, the time cost is low, and the proposed algorithm has a high F-measure and much lower power consumption than other algorithms, indicating that the proposed algorithm is effective.
Reduced-Order Extended Dissipative Filtering for Nonlinear Systems With Sensor Saturation via Interval Type-2 Fuzzy Model

The system nonlinearity, sensor saturation, and the uncertainty will hamper the analysis and affect the control performance. Filtering is a signal processing method which facilitates the system analysis and synthesis by signal estimation or noise suppression. To achieve generalized filtering problem for nonlinear systems with sensor saturations with lower computational burden, this article addresses the reduced-order extended dissipative filter design for nonlinear sensor-saturated system which is modeled by interval type-2 (IT2) T–S fuzzy system. For IT2 T–S fuzzy systems, the main challenge exists in the acquisition of the information in IT2 membership functions (MFs) for analysis and design. A membership-function-dependent (MFD) method is applied to capture the information of the MFs for reducing the conservativeness introduced by MFs not involved in the analysis. An extended dissipative filtering method, under imperfect premise matching (IPM) concept that the membership functions of the filter are different from those of the model, is proposed for sensor-saturated IT2 fuzzy systems. The proposed method has a high flexibility in parameters adjustment of both the filter and the design condition, including extended dissipative matrices, approximation MFs, and sensor saturation degree, one can freely choose the parameters according to the required performance of the fuzzy filter. A numerical example is given to demonstrate the effectiveness of the results.
A video summarization framework based on activity attention modeling using deep features for smart campus surveillance system

Like other business domains, digital monitoring has now become an integral part of almost every academic institution. These surveillance systems cover all the routine activities happening on the campus while producing a massive volume of video data. Selection and searching the desired video segment in such a vast video repository is highly time-consuming. Effective video summarization methods are thus needed for fast navigation and retrieval of video content. This paper introduces a keyframe extraction method to summarize academic activities to produce a short representation of the target video while preserving all the essential activities present in the original video. First, we perform fine-grain activity recognition using a realistic Campus Activities Dataset (CAD) by modeling activity attention scores using a deep CNN model. In the second phase, we use the generated attention scores for each activity category to extract significant video frames. Finally, we evaluate the inter-frame similarity index used to reduce the number of redundant frames and extract only the representative keyframes. The proposed framework is tested on different videos, and the experimental results show the performance of the proposed summarization process.
QuarantivityVR: Supporting Self-Embodiment for Non-HMD Users in Asymmetric Social VR Games

Abstract The prevalence of immersive head-mounted display (HMD) social virtual reality (VR) applications introduced asymmetric interaction among users within the virtual environment (VE). Therefore, researchers opted for (1) exploring the asymmetric social VR interaction dynamics in only co-located setups, (2) assigning interdependent roles to both HMD and non-HMD users, and (3) representing non-HMD users as abstract avatars in the VE. Therefore, we investigate the feasibility of supporting Self-Embodiment in an asymmetric VR interaction mode in a remote setup. To this end, we designed an asymmetric social VR game, QuarantivityVR , to (1) support sense of self-embodiment for non-HMD users in a remote setting by representing them as realistic full-body avatars within the VE, (2) augment visual-motor synchrony for the non-HMD users to increase their sense of agency and presence by detecting their motion through Kinect sensor and laptop’s webcam. During the game, each player performs three activities in succession, namely movie-guessing, spelling-bee, and answering mathematical questions. We believe that our work will act as a step towards the inclusion of a wide spectrum of users that can not afford full immersion and will aid researchers in creating enjoyable interactions for both users in the physical and virtual spaces.
Homogeneous Learning: Self-Attention Decentralized Deep Learning

Federated learning (FL) has been facilitating privacy-preserving deep learning in many walks of life such as medical image classification, network intrusion detection, and so forth. Whereas it necessitates a central parameter server for model aggregation, which brings about delayed model communication and vulnerability to adversarial attacks. A fully decentralized architecture like Swarm Learning allows peer-to-peer communication among distributed nodes, without the central server. One of the most challenging issues in decentralized deep learning is that data owned by each node are usually non-independent and identically distributed (non-IID), causing time-consuming convergence of model training. To this end, we propose a decentralized learning model called Homogeneous Learning (HL) for tackling non-IID data with a self-attention mechanism. In HL, training performs on each round’s selected node, and the trained model of a node is sent to the next selected node at the end of each round. Notably, for the selection, the self-attention mechanism leverages reinforcement learning to observe a node’s inner state and its surrounding environment’s state, and find out which node should be selected to optimize the training. We evaluate our method with various scenarios for two different image classification tasks. The result suggests that HL can achieve a better performance compared with standalone learning and greatly reduce both the total training rounds by 50.8% and the communication cost by 74.6% for decentralized learning with non-IID data.
Fuzzing+Hardware Performance Counters-Based Detection of Algorithm Subversion Attacks on Postquantum Signature Schemes

NIST is standardizing postquantum cryptography (PQC) algorithms that are resilient to the computational capability of quantum computers. Past works show malicious subversion with cryptographic software algorithm subversion attacks (ASAs) that weaken the implementations. We show that PQC digital signature (DS) codes can be subverted in line with previously reported flawed implementations (2008) (Bernstein et al., 2016) that generate verifiable, but less-secure signatures, demonstrating the risk of such attacks. Since all processors have built-in hardware performance counters (HPCs), there exists a body of work proposing a low-cost machine learning (ML)-based integrity checking of software using HPC fingerprints. However, such HPC-based approaches may not detect subversion of PQC codes. A miniscule percentage of qualitative inputs when applied to the PQC codes improves this accuracy to 98%. We propose gray-box fuzzing as a preprocessing step to obtain inputs to aid the proposed HPC-based method.
Toward a Period-specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts

Over the past few decades, large archives of paper-based historical documents, such as books and newspapers, have been digitized using the Optical Character Recognition (OCR) technology. Unfortunately, this broadly used technology is error-prone, especially when an OCRed document was written hundreds of years ago. Neural networks have shown great success in solving various text processing tasks, including OCR post-correction. The main disadvantage of using neural networks for historical corpora is the lack of sufficiently large training datasets they require to learn from, especially for morphologically-rich languages like Hebrew. Moreover, it is not clear what are the optimal structure and values of hyperparameters (predefined parameters) of neural networks for OCR error correction in Hebrew due to its unique features. Furthermore, languages change across genres and periods. These changes may affect the accuracy of OCR post-correction neural network models. To overcome these challenges, we developed a new multi-phase method for generating artificial training datasets with OCR errors and hyperparameters optimization for building an effective neural network for OCR post-correction in Hebrew.
Exquisitor at the Video Browser Showdown 2022

Exquisitor is the state-of-the-art large-scale interactive learning approach for media exploration that utilizes user relevance feedback at its core and is capable of interacting with collections containing more than 100M multimedia items at sub-second latency. In this work, we propose improvements to Exquisitor that include new features extracted at shot level for semantic concepts, scenes and actions. In addition, we introduce extensions to the video summary interface providing a better overview of the shots. Finally, we replace a simple keyword search featured in the previous versions of the system with a semantic search based on modern contextual representations.
Accounting for drop-out using inverse probability censoring weights in longitudinal clustered data with informative cluster size

Periodontal disease is a serious gum infection impacting half of the U.S. adult population that may lead to loss of teeth. Using standard marginal models to study the association between patient-level predictors and tooth-level outcomes can lead to biased estimates because the independence assumption between the outcome (periodontal disease) and cluster size (number of teeth per patient) is violated. Specifically, the baseline number of teeth of a patient is informative. In this setting a cluster-weighted generalized estimating equations (CWGEE) approach can be used to obtain unbiased marginal inference from data with informative cluster size (ICS). However, in many longitudinal studies of dental health, including the Veterans Affairs Dental Longitudinal Study, the rate of tooth-loss or tooth drop-out over time is also informative, creating a missing at random data mechanism. Here, we propose a novel modeling approach that incorporates the technique of inverse probability censoring weights into CWGEE with binary outcomes to account for ICS and informative drop-out over time. In an extensive simulation study we demonstrate that results obtained from our proposed method yield lower bias and excellent coverage probability, compared to those obtained from traditional methods which do not account for ICS or drop-out.
Accelerated Distributed Approximate Newton Method

Distributed second-order optimization, as an effective strategy for training large-scale machine learning systems, has been widely investigated due to its low communication complexity. However, the existing distributed second-order optimization algorithms, including distributed approximate Newton (DANE), accelerated inexact DANE (AIDE), and statistically preconditioned accelerated gradient (SPAG), are all required to precisely solve an expensive subproblem up to the target precision. Therefore, this causes these algorithms to suffer from high computation costs and this hinders their development. In this article, we design a novel distributed second-order algorithm called the accelerated distributed approximate Newton (ADAN) method to overcome the high computation costs of the existing ones. Compared with DANE, AIDE, and SPAG, which are constructed based on the relative smooth theory, ADAN's theoretical foundation is built upon the inexact Newton theory. The different theoretical foundations lead to handle the expensive subproblem efficiently, and steps required to solve the subproblem are independent of the target precision. At the same time, ADAN resorts to the acceleration and can effectively exploit the objective function's curvature information, making ADAN to achieve a low communication complexity. Thus, ADAN can achieve both the communication and computation efficiencies, while DANE, AIDE, and SPAG can achieve only the communication efficiency. Our empirical study also validates the advantages of ADAN over extant distributed second-order algorithms.
Quantum annealing with pairs of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mrow /><mml:mn>2</mml:mn></mml:msup><mml:mi mathvariant="normal">Σ</mml:mi></mml:mrow></mml:math> molecules as qubits

The rotational and fine structure of open-shell molecules in a $\Sigma$ electronic state gives rise to crossings between Zeeman states of different parity. These crossings become avoided in the presence of an electric field. We propose an algorithm that encodes Ising models into qubits defined by pairs of $^2\Sigma$ molecules sharing an excitation near these avoided crossings. This can be used to realize a transverse field Ising model tunable by an external electric or magnetic field, suitable for quantum annealing applications. We perform dynamical calculations for several examples with one- and two-dimensional connectivities. Our results demonstrate that the probability of obtaining valid annealing solutions is high and can be optimized by varying the annealing times.