Reviewerly/exHarmony huggingface dataset:
Analysis of Innovation Performance for Retail Banking Industry With the Hybrid Fuzzy Decision-Making Approach

This study aims to evaluate the innovation performance of the Turkish banking industry. For this purpose, eight different financial and nonfinancial criteria are identified as a result of literature review. Moreover, five biggest Turkish deposit banks are selected as alternatives. Interval type 2 fuzzy decision-making trial and evaluation laboratory (IT2 FDEMATEL) is taken into account to weight the dimensions. On the other side, to rank the alternatives, interval type 2 fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (IT2 FVIKOR) approach is considered. The findings show that market share and return on investment are the most important factors in the innovation performance of retail banking services. This situation gives information that the when banks are more successful in the market, they can make investment to the innovation more effectively. The main reason is that these banks have enough capacity to make this kind of investments. Thus, it is recommended that Turkish banks should first make an efficiency analysis to increase their profit margin so that their capacities can be improved. For this purpose, many different factors should be taken into account by Turkish banks, such as personnel competency and technological development. Within this scope, these banks should make financial analysis of the innovation effectively by generating a qualified team. However, necessary research and development activities should be conducted to reach this objective. Hence, it can be possible to minimize ineffective innovation for the banks.
Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization

In order to improve the adaptive management ability of virtual machine placement in cloud computing, an adaptive management and multi-objective optimization method for virtual machine placement in cloud computing is proposed based on particle swarm optimization (PSO). The objective optimization model of adaptive management of virtual machine placement in cloud computing is constructed by particle swarm evolution, and the global optimization control of adaptive management of virtual machine placement in cloud computing is carried out by introducing extremum perturbation operator. The global dynamic objective function of particle swarm optimization is constructed, and the global optimal solution of virtual machine in cloud computing is found by deconvolution algorithm, and the optimal position of particle swarm is searched in two-dimensional space. The multi-objective optimization problem of adaptive management of virtual machine placement is transformed into particle swarm optimization problem to realize adaptive management and multi-objective optimization of virtual machine placement in cloud computing. Simulation results show that the adaptive management of virtual machine placement in cloud computing using this method has better global optimization ability, better convergence of particle swarm optimization, and better performance of multi-objective optimization.
A New Similarity Model Based on Collaborative Filtering for New User Cold Start Recommendation

Collaborative filtering (CF) is one of the most popular approaches to building Recommender systems (RS) and has been extensively implemented in many online applications. But it still suffers from the new user cold start problem that users have only a small number of items interaction or purchase records in the system, resulting in poor recommendation performance. Thus, we design a new similarity model which can fully utilize the limited rating information of cold users. We first construct a new metric, Popularity-Mean Squared Difference, considering the influence of popular items, average difference between two user's common ratings and non-numerical information of ratings. Moreover, the second new metric, Singularity-Difference, presents the deviation degree of favor to items between two users. It considers the distribution of the similarity degree of co-ratings between two users as weight to adjust the deviation degree. Finally, we take account of user's personal rating preferences through introducing the mean and variance of user ratings. Experiment results based on three real-life datasets of MovieLens, Epinions and Netflix demonstrate that the proposed model outperforms seven popular similarity methods in terms of MAE, precision, recall and F1-Measure under new user cold start condition.
Extending Graph Patterns with Conditions

We propose an extension of graph patterns, referred to as conditional graph patterns and denoted as CGPs. In a CGP,one can specify a simple condition on each edge such that the edge exists if and only if the condition is satisfied. We show that CGPs allow us to catch missing links, increase the expressivity of graph functional dependencies, and provide a succinct representation of graph patterns. We settle the complexity of their consistency, matching, incremental matching and containment problems, in linear time,NP-complete,NP-complete and p2-complete, respectively. These tell us that despite the increased expressive power of CGPs, the matching and incremental matching problems for CGPs are no harder than their counterparts for conventional patterns. We develop algorithms for matching and incremental matching of CGPs, and for (incremental) multi-CGP matching and optimization. Using real-life and synthetic graphs, we empirically verify the efficiency and effectiveness of our algorithms.
Understanding the Past, Present, and Future of Design Fictions

Design fictions are increasingly important and prevalent within HCI, though they are created through diverse practices and approaches and take many diverse forms. The goal of this workshop is both to create an overview of this diversity and to move towards a shared vision of design fiction within the CHI community. With this goal in mind, we invite reports, analyses, and examples of design fictions. An outcome will be development of a summary of the current state-of-the-art seeded by a diversity of perspectives within CHI, a descriptive orientation to this important domain of practices and outcomes, and a proposed set of evaluation guidelines for reviewers of design fiction submissions.
Effect of cybercrime laws on protecting citizens and businesses in the United Arab Emirates (UAE)

Purpose The purpose of this paper is to explore the extent to which cybercrime laws protect citizens and businesses in the United Arab Emirates (UAE). Pertinent questions over the lax regulatory environment and incomprehensible cybersecurity policies have influenced the discussions. Design/methodology/approach This paper will first offer a global outlook of cybersecurity laws and legislation. The global outlook will present the basis for examining best practices that the UAE could emulate. The paper will then examine the legislative landscape of cyber laws in the UAE, including cross-country comparisons. The comparisons are critical, as the country’s cybercrime laws are in their infancy phase. Findings The UAE has taken decisive and proactive measures to deter the threat of cybercrimes and cyberattacks. The UAE strategy comprehensive strategy has been effective in protecting the economy and populations from the adverse effects of cybercrimes. The success lies in the enactment of comprehensive and streamlines laws and regulations with harsher penalties. The stringent legal measures, including longer jail terms, stiffer fines and deportation of foreigners, have ensured robust deterrence to cybercriminals. Originality/value The analysis has shown that the UAE has a higher score of preparedness against cybercrimes and cyberattacks. The UAE has specifically crafted a broader and effective legislative framework of cybercrime laws. Although the UAE has comprehensive cybercrime laws, the remarkable level of technological advances in the country makes citizens and businesses lucrative targets. The UAE now has the burden of doubling down its legal efforts to deter emerging cybersecurity risks.
Domain Adaptive Imitation Learning

We study the question of how to imitate tasks across domains with discrepancies such as embodiment, viewpoint, and dynamics mismatch. Many prior works require paired, aligned demonstrations and an additional RL step that requires environment interactions. However, paired, aligned demonstrations are seldom obtainable and RL procedures are expensive. We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch. Informally, DAIL is the process of learning how to perform a task optimally, given demonstrations of the task in a distinct domain. We propose a two step approach to DAIL: alignment followed by adaptation. In the alignment step we execute a novel unsupervised MDP alignment algorithm, Generative Adversarial MDP Alignment (GAMA), to learn state and action correspondences from \emph{unpaired, unaligned} demonstrations. In the adaptation step we leverage the correspondences to zero-shot imitate tasks across domains. To describe when DAIL is feasible via alignment and adaptation, we introduce a theory of MDP alignability. We experimentally evaluate GAMA against baselines in embodiment, viewpoint, and dynamics mismatch scenarios where aligned demonstrations don't exist and show the effectiveness of our approach.
Enabling identity-based authorized encrypted diagnostic data sharing for cloud-assisted E-health information systems

Electronic health (e-health) information systems rely on cloud computing technologies to provide massive medical data storage services. Particularly, to ensure the confidentiality of diagnostic results, sensitive diagnostic data need to be encrypted before being uploaded to a cloud server. Hence, enabling encrypted diagnostic data sharing efficiently is a demanding issue. To this end, we propose an identity-based authorized searchable encryption scheme (IBASE), without incurring substantial certificate management costs. IBASE enables a doctor to authorize an assistant to handle the tricky encrypted diagnostic data sharing with patients in cloud-assisted e-health information systems, by using identity-based encryption with keyword search, which dramatically alleviates the work intensity of the doctor. As a consequence, any patient could retrieve his/her diagnostic reports in a confidential way, by submitting a trapdoor of the corresponding keyword. We formally prove the security of IBASE, and IBASE resists inside keyword guessing attacks (IKGA), any shared encrypted diagnostic data cannot be leaked to the cloud server. The performance comparison demonstrates that IBASE is practical for mobile cloud-assisted e-health information systems.
Classifying Emergency Tweets for Disaster Response

During disaster events such as floods, landslides, earthquakes, tsunamis, fire hazards, etc., social media platforms provide easy and timely access to information regarding the ongoing crisis events and thereby become an essential vehicle of information sharing. During such events, great amounts of such socially generated data becomes available, which can be accessed and processed to extract situational awareness insights. These insights, in turn, can be used to enhance the effectiveness and efficiency of disaster response in order to minimize the loss of lives and damage to property. People actively use social platforms like Facebook and Twitter to post information related to crisis events. Further, these platforms provide people the location and safety status of their family and friends during such events. Twitter, the microblogging platform, witnesses thousands of informally written tweets during crisis events, and since it provides high-level APIs to access its near real-time feed, it has become the primary source of data for researchers. It is generally observed that there is an exponential burst in the number of tweets during an ongoing crisis event. This sudden burst makes the task of monitoring, identifying, and processing each tweet virtually impossible for a human. However, such voluminous data can be processed using various machine learning and natural language processing techniques in coordination with a certain level of human interventions. This paper is focused on designing a semi-automated artificial intelligence-based classifier, which can classify the plethora of disaster-related tweets into various categories such as community needs, loss of lives, damage.
Counter-examples generation from a positive unlabeled image dataset

This paper considers the problem of positive unlabeled (PU) learning. In this context, we propose a two-stage GAN-based model. More specifically, the main contribution is to incorporate a biased PU risk within the standard GAN discriminator loss function. In this manner, the discriminator is constrained to steer the generator to converge towards the unlabeled samples distribution while diverging from the positive samples distribution. Consequently, the proposed model, referred to as D-GAN, exclusively learns the counter-examples distribution without prior knowledge. Experimental results on simple and complex image datasets demonstrate that our approach outperforms state-of-the-art PU methods without prior by overcoming issues such as sensitivity to prior knowledge or first-stage overfitting.
United we stand: Accruals in strength-based argumentation

Argumentation has been an important topic in knowledge representation, reasoning and multi-agent systems during the last twenty years. In this paper, we propose a new abstract framework where arguments are associated with a strength, namely a quantitative information which is used to determine whether an attack between arguments succeeds or not. Our Strength-based Argumentation Framework (StrAF) combines ideas of Preference-based and Weighted Argumentation Frameworks in an original way, which permits to define acceptability semantics sensitive to the existence of accruals between arguments. The question of accruals arises in situations where several arguments defending the same position (but from different points of view) against another argument are unable to individually defeat this argument, but could do it collectively if they combine their strengths. We investigate some of the theoretical and computational properties of our new framework and semantics, and present a reasoning algorithm that is based on a translation of the problem into pseudo-boolean constraint satisfaction. This paper proposes an intuitive framework which allows strength compensations in an argumentation context where attacks may not succeed, completed by an approach which detects accruals throughout the reasoning process without requiring the elicitation of all compensatory combinations of arguments as an input.
Robust and discriminative zero-watermark scheme based on invariant features and similarity-based retrieval to protect large-scale DIBR 3D videos

Digital rights management (DRM) of depth-image-based rendering (DIBR) 3D video is an emerging area of research. Existing schemes for DIBR 3D video cause video distortions, are vulnerable to severe signal and geometric attacks, cannot protect 2D frames and depth maps independently, or have difficulty handling large-scale videos. To address these issues, a novel zero-watermark scheme based on invariant features and similarity-based retrieval to protect DIBR 3D video (RZW-SR) is proposed in this study. In RZW-SR, invariant features are extracted to generate master and ownership shares to provide distortion-free, robust and discriminative copyright identification under various attacks. Different from conventional zero-watermark schemes, our proposed scheme stores features and ownership shares correlatively and designs a similarity-based retrieval phase to provide effective solutions for large-scale videos. In addition, flexible mechanisms based on attention-based fusion are designed to protect 2D frames and depth maps, either independently or simultaneously. The experimental results demonstrate that RZW-SR has superior DRM performance compared to existing schemes. First, RZW-SR can obtain the ownership shares relevant to a particular 3D video precisely and reliably for effective copyright identification of large-scale videos. Second, RZW-SR ensures lossless, precise, reliable and flexible copyright identification for 2D frames and depth maps of 3D videos.
The Application of Human Comprehensive Development Theory and Deep Learning in Innovation Education in Higher Education

Since the core of talent training in modern society has changed from allowing students to master knowledge to adapting them to a lifelong learning society, this paper studied the innovation education in higher education combining human comprehensive development theory and deep learning. First, this paper described the training needs of innovative education talents. Second, based on the human comprehensive development theory and deep learning, it put forward the views on innovative quality education in colleges and evaluates the student’s deep learning status by designing a deep learning evaluation model. From the perspective of students, a questionnaire for student-side use was designed according to the deep learning evaluation scale, so that students could understand their deep learning status and make improvements based on their current situation, and improve the level of deep learning, thereby obtaining the inspiration of the innovation education in higher education. The research shows that the students’ mastery of knowledge and skills is the highest, indicating that the mastery of students in this respect has reached the standard. Although the mean value obtained by the process and method is greater than the median, it is almost the same as the median, which indicates that the students’ mastery of the process and method is still lacking, and further efforts are needed. In terms of psychology and values, the mean value is the lowest in the three and less than the median, indicating that students are the weakest in this respect and further improvement is required. In addition, a new teaching evaluation method was designed based on human comprehensive development theory and deep learning, contributing to innovation education in higher education in China.
A real-time path-planning algorithm with extremely tight maneuvering capabilities for hyper-redundant manipulators

This paper presents a simple, effective and robust geometrical approach for path-planning of redundant/hyper-redundant manipulators in real-time within confined spaces cluttered with obstacles. A discretized path for point robots to the goal gives all the information for navigation of the manipulator. As long as it is physically possible, the employment of maneuvering space is maximized by making the links almost pinch the walls of the convoluted terrain. The maneuvering ability of the method fully utilizing maneuvering space is far beyond those of others proposed in the literature. The exemplary computer simulations verify the effectiveness of the method.
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting

We propose a method for natural language generation, choosing the most representative output rather than the most likely output. By viewing the language generation process from the voting theory perspective, we define representativeness using range voting and a similarity measure. The proposed method can be applied when generating from any probabilistic language model, including n-gram models and neural network models. We evaluate different similarity measures on an image captioning task and a machine translation task, and show that our method generates longer and more diverse sentences, providing a solution to the common problem of short outputs being preferred over longer and more informative ones. The generated sentences obtain higher BLEU scores, particularly when the beam size is large. We also perform a human evaluation on both tasks and find that the outputs generated using our method are rated higher.
ClothFace: A Passive RFID-Based Human-Technology Interface on a Shirtsleeve

This paper introduces ClothFace, a shirtsleeve-integrated human-technology interface platform, which comprises two wrist antennas and three radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. The platform prototype, which is created on a shirtsleeve by cutting the antennas and antenna-IC interconnections from copper tape, can be used for push button and swipe controlling. Each IC can be activated, i.e., electrically connected to the two antennas, by touching the IC. These ICs can act as wireless input buttons to the technology around us. Due to the used passive ultrahigh-frequency (UHF) RFID technology, there is no need for clothing-integrated energy sources, but the interface platform gets all the needed energy from an external RFID reader. The platform prototype was found to be readable with an external RFID reader from all directions at distances of 70–80 cm. Further, seven people giving altogether 1400 inputs tested the prototype sleeves on a table and on body. In these first tests, 96–100% (table) and 92–100% (on-body) success rates were achieved in a gamelike testing setup. Further, the platform was proved to be readable with an off-the-shelf handheld RFID reader from a distance of 40 cm. Based on these initial results, this implementation holds the potential to be used as a touch interface blended into daily clothing, as well as a modular touch-based interaction platform that can be integrated into the surfaces of electronic devices, such as home appliances.
Systematic Agile Development in Regulated Environments

For established domains within highly regulated environments, a systematic approach is needed to scale agile methods and assure compliance with regulatory requirements. This paper presents a structured method to designing such a systematic approach which works adequately in small agile teams. It is independent of the underlying method such as Scrum, Kanban, etc., and is scalable to more and bigger teams or even entire subsidiaries. It is based on a compliance and a quality risk dimension which are both needed to fit regulatory requirements. The approach has been validated in the financial IT context with more than 100 developers in one subsidiary.
Effective public service delivery supported by time-decayed Bayesian personalized ranking

Information overload is becoming a prominent problem that hinders the effectiveness of e-government. Personalized e-government services with recommendation techniques may provide a solution by helping users find the target service items based on her interaction histories. Most existing e-government recommendation methods are rating based, but the ratings in e-government are unavailable. Besides, the interactions in e-government are sequence-dependent while the sequential information are usually ignored. These two problems negatively affect the efficiency of the service distribution of e-government platforms. In this paper, we studied the sequential implicit feedback recommendation problem in e-government and design a novel learning algorithm called time-decayed Bayesian personalized ranking. Time-decayed BPR captures the sequence effects using time-decayed Ordinal Utility and inherits the seminal pairwise learning framework of BPR. It digests users’ sequential behavior naturally, and combines the benefits of time-aware information (i.e. sequential behavior) and time-invariant information (i.e. general taste). An empirical analysis of real-world e-government datasets shows that our time-decayed-BPR approach improves the performance significantly regarding various evaluation metrics compared with the state-of-the-art baseline methods.
Covid-19: Is local contact tracing the answer?

As the government shrinks the national scheme for contact tracing, Gareth Iacobucci asks local public health officials about their chances of success