Reviewerly/exHarmony huggingface dataset:
Session-Based Recommendation by combining Probabilistic Models and LSTM

In this paper, we present the approach, we used as team ”DataLab HMU.GR”, for the ACM RecSys Challenge 2022 [1]. The challenge aims to predict the item that was purchased for a given sequence of item views (session). The full dataset, provided by Dressipi, consists of 1.1 million online retail sessions. Our proposed method, that solves the Session-Based Recommendation problem, relies on an efficient deterministic system based on a weighted combination of Probabilistic models and an LSTM neural network. Probabilistic models learn the transition probabilities between item-item interactions of each session, that are used to predict the purchase probability of an item in a new session. The LSTM neural network takes as input the context representation of the items in a session and a candidate item and predicts the purchase probability of the candidate item. The experimental results demonstrate the high performance and the computational efficiency of the probabilistic models. Our submission achieved the 13th rank and an overall score of 0.1963 in the final competition results. We release our source code at: https://github.com/cpanag79/recsys-Challenge-2022.
Effective fuzzy joint mutual information feature selection based on uncertainty region for classification problem

Classification problem widely exists in real-world applications. Unfortunately, data quality is the main challenge of the classification models, especially when the data includes irrelevant and redundant features. Feature selection (FS) is an effective preprocessing technique to enhance the quality of the data. For this, an integration of information theory and fuzzy sets introduced powerful measures, such as fuzzy information measures, to develop many feature selection methods. However, estimating fuzzy information measures is not only costly in the space and runtime but also may be affected by the bias between the certainty and uncertainty regions. This paper proposes a novel instance selection based on uncertainty region (ISUR) to overcome these limitations. Then, a state-of-the-art FS method, called fuzzy joint mutual information (FJMI), has been adapted to design an effective FS method, called fuzzy joint mutual information feature selection based on uncertainty region (FJMIUR). The proposed method consists of two processes: instance selection and feature selection. The former selects the uncertainty region that improves the estimation of fuzzy information measures and reduces the consumed cost while the latter selects the most significant features. Using 20 real-world classification datasets, comparative experiments, including well-known and state-of-the-art FS methods, were conducted to evaluate the effectiveness of FJMIUR. The results show the outperformance of FJMIUR in most cases according to six classification measures, average percentage of selected features, space, and runtime.
AutoGAN-Synthesizer: Neural Architecture Search for Cross-Modality MRI Synthesis

Considering the difficulty to obtain complete multi-modality MRI scans in some real-world data acquisition situations, synthesizing MRI data is a highly relevant and important topic to complement diagnosis information in clinical practice. In this study, we present a novel MRI synthesizer, called AutoGAN-Synthesizer, which automatically discovers generative networks for cross-modality MRI synthesis. Our AutoGAN-Synthesizer adopts gradient-based search strategies to explore the generator architecture by determining how to fuse multi-resolution features and utilizes GAN-based perceptual searching losses to handle the trade-off between model complexity and performance. Our AutoGAN-Synthesizer can search for a remarkable and light-weight architecture with 6.31 Mb parameters only occupying 12 GPU hours. Moreover, to incorporate richer prior knowledge for MRI synthesis, we derive K-space features containing the low- and high-spatial frequency information and incorporate such features into our model. To our best knowledge, this is the first work to explore AutoML for cross-modality MRI synthesis, and our approach is also capable of tailoring networks given either different multiple modalities or just a single modality as input. Extensive experiments show that our AutoGAN-Synthesizer outperforms the state-of-the-art MRI synthesis methods both quantitatively and qualitatively. The code are available at https://github.com/HUuxiaobin/AutoGAN-Synthesizer .
The Role of Non-Technical Skills in the Software Development Market

Software development is an intrinsic collaborative profession, which demands a rich set of technical and non-technical skills. In particular, the recent changes that shifted the software development practice (from a collaborative in-person based activity to an intrinsic geographically distributed one) accelerated the need of non-technical skills. In this work, we aimed to draw the current landscape of non-technical skills in the software development area. To achieve this goal, we conducted a two-phase study. We started by inspecting 566 job post advertisement to assess how often these skills are requested. In the second phase, we interviewed 15 junior developers to find out which non-technical skills they think are required in their organizations, and to understand how they exercise these non-technical skills. Our results suggest that 98,9% of the job posts mentioned at least one non-technical skill, averaging 6.30 non-technical skills per job post. Our interviewees believe that non-technical skills help them to understand the organizational culture, the team dynamics, and their careers. In summary, we believe that our work contributes to pile additional evidence on the need that software professionals have in mastering these non-technical skills.
ASP-based Multi-shot Reasoning via DLV2 with Incremental Grounding

DLV2 is an AI tool for Knowledge Representation and Reasoning which supports Answer Set Programming (ASP) – a logic-based declarative formalism, successfully used in both academic and industrial applications. Given a logic program modelling a computational problem, an execution of DLV2 produces the so-called answer sets that correspond one-to-one to the solutions. The computational process relies on the typical Ground&Solve approach where the grounding step transforms the input program into a new, equivalent ground program, and the subsequent solving step applies propositional algorithms to search for the answer sets. Recently, emerging applications in contexts such as stream reasoning and event processing demand for multi-shot reasoning: here, the system is expected to be reactive while repeatedly executed over rapidly changing data. In this work, we present a new incremental reasoner obtained from the evolution of DLV2 towards multi-shot reasoning. Rather than restarting the computation from scratch, the system remains alive and incrementally handles the internal grounding process: in a completely transparent fashion for the user, at each shot, it reuses previous computations for building and maintaining a large, more general ground program, from which a smaller yet equivalent portion is determined and used for computing answer sets. We describe the system, its usage, its applicability and performance in some practically relevant domains.
Logical separability of labeled data examples under ontologies

Finding a logical formula that separates positive and negative examples given in the form of labeled data items is fundamental in applications such as concept learning, reverse engineering of database queries, generating referring expressions, and entity comparison in knowledge graphs. In this paper, we investigate the existence of a separating formula for data in the presence of an ontology. Both for the ontology language and the separation language, we concentrate on first-order logic and the following important fragments thereof: the description logic ALCI, the guarded fragment, the two-variable fragment, and the guarded negation fragment. For separation, we also consider (unions of) conjunctive queries. We consider several forms of separability that differ in the treatment of negative examples and in whether or not they admit the use of additional helper symbols to achieve separation. Our main results are model-theoretic characterizations of (all variants of) separability, the comparison of the separating power of different languages, and the investigation of the computational complexity of deciding separability.
Social rather than physical crowding reduces the required interpersonal distance in virtual environments

Interpersonal distance plays an important role in human social interaction. With the increasing usage of virtual reality in social interaction, people's interpersonal distance in virtual space attracts great attention. It remains unclear whether and to what extent human-required interpersonal distance is altered by crowded virtual scenes. In this study, we manipulated crowd density in virtual environments and used the classical stop-distance paradigm to measure required interpersonal distances at different crowd densities. We found that people's required interpersonal distance decreased with increased social crowdedness but not with physical crowdedness. Moreover, the decrease of two types of interpersonal distance was associated with the globally averaged crowd density rather than local crowd density. The reduction is not due to the imitation of other virtual humans in the crowd. Moreover, we developed a model to describe the quantitative relationships between the crowdedness of the environment and the required interpersonal distance. Our finding provides insights into designing user-friendly virtual humans in metaverse virtual worlds.
A pruning algorithm with relaxed conditions for high-order neural networks based on smoothing group <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2238" altimg="si242.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> regularization and adaptive momentum

To enhance the sparseness of the network, improve its generalization ability and accelerate its training, we propose a novel pruning approach for sigma-pi-sigma neural network (SPSNN) under the relaxed condition by adding smoothing group L 1 / 2 regularization and adaptive momentum. The main strength of this method is that it can prune both the redundant nodes between groups in the network, and also the redundant weights of the non-redundant nodes within the group, so as to achieve the sparseness of the network. Another strength is that the non-smooth absolute value function in the traditional L 1 / 2 regularization method is replaced by a smooth function. This reduces the oscillations of learning and enables us to more effectively prove the convergence of the proposed algorithm. Finally, the numerical simulation results demonstrate the effectiveness of the proposed algorithm.
Research on the Innovation of the Internet of Things Business Model under the New Scenario of Metaverse

The biggest feature of the Internet of things technology in the new scene of metasverse is that everything can be interconnected and everything can be created. And marketing activities have always shown a trend of moving forward with technology. This paper expounds the relevant theories of Internet of things technology and metasverse and their relationship. Literature analysis method, cluster analysis method and inductive summary method are used to study the new scenario of metaverse marketing supported by Internet of Things technology, which will improve the connection quality between people and greatly enhance the communication efficiency of marketing activities. Metasverse emphasizes real-time interaction and information interconnection and mainly relies on the Internet of things to provide technical support. When it is applied to the business marketing field, the customer manager will no longer be limited to simple voice and simple image, but the signals released by the customer's micro expression and body language can also be effectively perceived.Through analysis and research, this paper concludes that the Internet of things technology has brought new benefits to business marketing under the new scene of metasverse, mainly including:get more reliable marketing data, gain more visual data processing capabilities, realize full traceability of product circulation, achieve better interaction with consumers. The new development trend of Internet of things marketing under the new metasverse scenario mainly includes:marketing plan will be more accurate, marketing activities will be value oriented, marketing services will be more timely, marketing campaigns will be scenario based, marketing activities will be decentralized, marketing technology innovation will be accelerated.
Online incentive mechanism for task offloading with privacy-preserving in UAV-assisted mobile edge computing

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to provide low-latency mobile edge computing (MEC) services. To fully utilize the potential of UAV-assisted MEC in practice, both technical and economic challenges need to be addressed: how to optimize UAV trajectory for online task offloading and incentivize the participation of UAVs without compromising the privacy of user equipment (UE). In this work, we consider unique features of UAVs, i.e., high mobility as well as limited energy and computing capacity, and propose a privacy-preserving auction framework, Ptero, to schedule offloading tasks on the fly and incentivize UAVs' participation. Specifically, Ptero first decomposes the online task offloading problem into a series of one-round problems by scaling the UAV's energy constraint into the objective. To protect UE's privacy, Ptero calculates UAV's coverage based on subset-anonymity. At each round, Ptero schedules UAVs greedily, computes remuneration for working UAVs, and processes unserved tasks in the cloud to maximize the system's utility (i.e., minimize social cost). Theoretical analysis proves that Ptero achieves truthfulness, individual rationality, computational efficiency, privacy preserving and a non-trivial competitive ratio. Trace-driven evaluations further verify that Ptero can reduce the social cost by up to 116% compared with four state-of-the-art algorithms.
Analytical ray transfer matrix for the crystalline lens

We present the formulation of a paraxial ray transfer or ABCD matrix for onion-type GRIN lenses. In GRIN lenses, each iso-indicial surface (IIS) can be considered a refracting optical surface. If each IIS is a shell or layer, the ABCD matrix of a GRIN lens is computed by multiplying a typically high number of translation and refraction matrices corresponding to the K layers inside the lens. Using a differential approximation for the layer thickness, this matrix product becomes a sum. The elements A, B, C, and D of the approximated GRIN ray transfer matrix can be calculated by integrating the elements of a single-layer matrix. This ABCD matrix differs from a homogeneous lens matrix in only one integration term in element C, corresponding to the GRIN contribution to the lens power. Thus the total GRIN lens power is the sum of the homogeneous lens power and the GRIN contribution, which offers a compact and simple expression for the ABDC matrix. We then apply this formulation to the crystalline lens and implement both numerical and analytical integration procedures to obtain the GRIN lens power. The analytical approximation provides an accurate solution in terms of Gaussian hypergeometric functions. Last, we compare our numerical and analytical procedures with published ABCD matrix methods in the literature, and analyze the effect of the iso-indicial surface's conic constant (Q) and inner curvature gradient (G) on the lens power for different lens models.
Display Rotation for Reducing Motion Sickness Caused by Using VR in Vehicles

Virtual Reality systems have the potential to change the way we interact with digital content in vehicles. However, wearing a VR headset in a vehicle may increase the chances of experiencing motion sickness. There is an open question as to how to convey visual motion alongside VR content to reduce motion sickness. This research explores possible countermeasures to alleviate motion sickness by delivering visual cues of physical motion through the position of virtual displays, implicitly conveying the motion without the need for additional distracting visual cues, which could alleviate motion sickness. The results showed that such visual cues significantly reduced symptoms associated with motion sickness. We expected our findings to lead to concrete suggestions to optimize VR user experiences and user comfort in vehicles.
Digital Skills Framework in Higher Education

Digital skills should be incorporated in the curriculum as the skills are essential attributes graduates should acquire before joining the workforce.Understanding the digital skills framework and abilities among learners may help educators create suitable learning outcomes, activities, and assessments to enable the learners to acquire these skills.This study shares expert judgements on a digital skills instrument (DSI) on content validation framework according to sufficiency, clarity, coherence, and relevance.A pre-test involving 218 students from various disciplines was conducted and Cronbach's Alpha was used to measure the validity and reliability of the DSI.The overall result indicated a strong reliability.Clarity was the characteristic garnering the highest value, followed by coherence, relevance, and sufficiency.The DSI was revised and 28 items encompassing five components were proposed following the feedback of: information literacy, computer and technology literacy, digital communication/collaboration skill, digital identity and well-being, and digital ethic.Based on the pre-test, all the components indicated high reliability, ranging from 0.847 to 0.958, and all of the items were retained to measure digital skills.Overall, the experts' evaluations and pre-tests suggested that the DSI and its five components as the digital skills framework can be used for measuring digital skills.
Benchmarking distance-based partitioning methods for mixed-type data

Abstract Clustering mixed-type data, that is, observation by variable data that consist of both continuous and categorical variables poses novel challenges. Foremost among these challenges is the choice of the most appropriate clustering method for the data. This paper presents a benchmarking study comparing eight distance-based partitioning methods for mixed-type data in terms of cluster recovery performance. A series of simulations carried out by a full factorial design are presented that examined the effect of a variety of factors on cluster recovery. The amount of cluster overlap, the percentage of categorical variables in the data set, the number of clusters and the number of observations had the largest effects on cluster recovery and in most of the tested scenarios. KAMILA, K-Prototypes and sequential Factor Analysis and K-Means clustering typically performed better than other methods. The study can be a useful reference for practitioners in the choice of the most appropriate method.
Privacy protection in social applications: A ciphertext policy attribute‐based encryption with keyword search

In a highly evolved big data era, intelligent data analysis can improve social operation efficiency and save resources. However, it also brings masses of conflicts, such as malicious mining and abuse of personal privacy information. This paper introduces a privacy protection scheme for social applications. In this scheme, attribute based searchable encryption is used to defend the security of confidential data and ensure the availability of data. Moreover, the access control structure of ciphertext strategy can meet the needs of data sharing in social applications. Security analysis shows that the scheme does not disclose privacy information in index ciphertext, search trapdoor, and equality test. Compared with plaintext information upload and sharing, the additional performance overhead caused by the scheme is acceptable. The scheme can be actually deployed in social applications.
Duality of generalized twisted Reed-Solomon codes and Hermitian self-dual MDS or NMDS codes

Self-dual MDS and NMDS codes over finite fields are linear codes with significant combinatorial and cryptographic applications. In this paper, firstly, we investigate the duality properties of generalized twisted Reed-Solomon (abbreviated GTRS) codes in some special cases. Then, a new systematic approach is proposed to obtain Hermitian self-dual (+)-GTRS codes, and furthermore, necessary and sufficient conditions for a (+)-GTRS code to be Hermitian self-dual are presented. Finally, several classes of Hermitian self-dual MDS and NMDS codes are constructed with this method.
Variational Autoencoders for Medical Image Retrieval

This paper presents an approach based on Variational Autoencoders (VAEs) for unsupervised learning of deep features for Content-Based Medical Image Retrieval (CBMIR). We show that this unsupervised approach can yield better results than the predominant supervised approach based on classification, and can even be used in combination with the classification approach, resulting in a second, mixed model. Despite the VAEs retrieving images more visually similar, the evaluation methodology usually employed in literature is not able to reveal this advantage, which is important for CBMIR. We then propose a new evaluation method based on hidden classes and show that it reflects the visual similarity of the retrieved images better than the traditional evaluation.
Thermal to Visual Person Re-Identification Using Collaborative Metric Learning Based on Maximum Margin Matrix Factorization

• We address the problem of thermal to visual person re-identification. • We propose collaborative metric learning method to address the problem. • The proposed collaborative metric learning uses maximum margin matrix factorization. • We obtain the more generalised metric due to the maximum margin. • Proposed method outperforms the baseline algorithms in few-shot learning settings. Thermal to visual person re-identification (T2V-ReID) is a cross-domain image retrieval problem. In this problem, the matching of a person’s image takes place, where the image is taken by different cameras (thermal and visual) at different times. This problem has numerous applications in night-time security surveillance. It is challenging due to the large intra-class variations and cross-domain discrepancies. Recently, deep metric learning methods are proposed for this problem. Still, there is a scope to improve the metric learning by generalizing the metric. In this paper, we have proposed the collaborative metric learning using Maximum Margin Matrix Factorization. It uses the group-wise similarities and collaboratively predicts the similarities. We can learn a more generalized metric by utilizing the maximized margin in this method. The proposed method is tested on the RegDB and RGB-D-T data sets, and the method outperforms the existing works in the few-shot learning settings.