Reviewerly/exHarmony huggingface dataset:
Advantages and Disadvantages of E-Learning Innovations during COVID-19 Pandemic in Higher Education in Poland

The paper presents the results of the research on the advantages and disadvantages of innovative e-learning during the COVID-19 pandemic in higher education.In the first stage, an internet questionnaire was used for the study.The research was carried out in Poland on a sample of 621 students.The results allowed the researchers to determine variables that are important for the proper implementation of innovative e-learning, and the emergent perspectives for this form of education after the end of the COVID-19 pandemic.Among the most significant disadvantages, there was a lack of direct contact with colleagues, difficulties with teaching practical subjects, lack of direct contact with the teacher, and too much time spent in front of a computer/telephone or other mobile device.The most important correlations included the following: students satisfied with e-learning assess the advantages highly and the disadvantages lower; all the advantages of innovative e-learning are positively correlated with the student's assessment of the ease of acquiring content in e-learning, and negatively correlated with the student's assessment of the ease of acquiring content in traditional education; the easier the student assimilates innovative e-learning content, the higher the student's evaluation of the advantages of e-learning; students who perceive e-learning content as difficult to absorb have low motivation to learn remotely; the better the student knows information technology, the more highly the student evaluates the advantages of innovative e-learning; the better resources to participate in e-learning activities the student has, the higher the student's evaluation of the advantages of e-learning; the more often innovative e-learning solutions are used in a given university, the better the student assesses the advantages of e-learning.
BabelRelate! A Joint Multilingual Approach to Computing Semantic Relatedness

We present a knowledge-rich approach to computing semantic relatedness which exploits the joint contribution of different languages. Our approach is based on the lexicon and semantic knowledge of a wide-coverage multilingual knowledge base, which is used to compute semantic graphs in a variety of languages. Complementary information from these graphs is then combined to produce a 'core' graph where disambiguated translations are connected by means of strong semantic relations. We evaluate our approach on standard monolingual and bilingual datasets, and show that: i) we outperform a graph-based approach which does not use multilinguality in a joint way; ii) we achieve uniformly competitive results for both resource-rich and resource-poor languages.
A Comparative Analysis of Decision Trees Vis-`a-vis Other Computational Data Mining Techniques in Automotive Insurance Fraud Detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers.Some of the computational techniques popularly used in the context of financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry.We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data.While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.
On Completeness Classes for Query Evaluation on Linked Data

The advent of the Web of Data kindled interest in link-traversal (or lookup-based) query processing methods, with which queries are answered via dereferencing a potentially large number of small, interlinked sources. While several algorithms for query evaluation have been proposed, there exists no notion of completeness for results of so-evaluated queries. In this paper, we motivate the need for clearly-defined completeness classes and present several notions of completeness for queries over Linked Data, based on the idea of authoritativeness of sources, and show the relation between the different completeness classes.
Exact Lifted Inference with Distinct Soft Evidence on Every Object

The presence of non-symmetric evidence has been a barrier for the application of lifted inference since the evidence destroys the symmetry of the first-order probabilistic model. In the extreme case, if distinct soft evidence is obtained about each individual object in the domain then, often, all current exact lifted inference methods reduce to traditional inference at the ground level. However, it is of interest to ask whether the symmetry of the model itself before evidence is obtained can be exploited. We present new results showing that this is, in fact, possible. In particular, we show that both exact maximum a posteriori (MAP) and marginal inference can be lifted for the case of distinct soft evidence on a unary Markov Logic predicate. Our methods result in efficient procedures for MAP and marginal inference for a class of graphical models previously thought to be intractable.
On the Effects of Computer-assisted Teaching on Learning Results Based on Blended Learning Method

With the rapid development of the computer network technology, the blended learning method based on computer-assisted teaching has also been booming. This paper analyzes the connotations of blended learning and the architecture and main functions of the Moodle system by using research methods such as litera-ture analysis, development and research method and sample survey method and then establishes a blended learning model based on the Moodle platform. Then, with the course of C Programming Language as an example, this paper gives de-tailed design of the process and implementation plan of the blended learning model based on the Moodle platform. At last, this paper investigates and analyzes the teaching effects among teachers and students participating in the learning ac-tivity, and the results show that the blended learning model based on the Moodle platform is helpful to improving the teaching effects and can serve as a reference for the innovation of teaching models and methods.
Hybrid approach for human posture recognition using anthropometry and BP neural network based on Kinect V2

When it comes to the studies concerning human-computer interaction, human posture recognition which was established on the basis of Kinect is widely acknowledged as a vital studying field. However, there exist some drawbacks in respect of the studying methods nowadays, for instance, limitations, insignificantly postures which can be recognized as well as the recognition rate which is relatively low. This study proposed a brand new approach which is hybrid in order to recognize human postures. The approach synthetically used depth data, skeleton data, knowledge of anthropometry, and backpropagation neural network (BPNN). First, the ratio of the height of the human head and that of body posture is ought to be evaluated. The distinguished four types of postures according to the ratio were standing, sitting or kneeling, sitting cross-legged, and other postures. Second, sitting and kneeling were judged according to the 3D spatial relation of the special points. Finally, feature vectors were extracted, transformed, and input to the BPNN according to the characteristics of the other postures, and bending and lying were recognized. Experiments proved the timeliness and robustness of the hybrid approach. The recognition accuracy was high, in which the average value was 99.09%.
An Efficient Collision Power Attack on AES Encryption in Edge Computing

Edge computing has become a promising paradigm for the context-aware and delay-sensitive IoT data analytics.For the sake of security, some cryptographic algorithms such as AES, RSA, and so on, are employed for the encryption communication and authentication.The collision power attack is a typical physical attack to recover the secret key of the AES algorithm.However, almost all collision attacks aim at the detection of internal collisions caused by the output of S-boxes, and the linear layers are not concerned with those protected implementations.The relation between the mask and the masked data has been given little attention and stays as is, where the leakages still exist.In this paper, we focus on three typical AES implementations in edge computing, and propose a new type of collision attack by making use of leakages from linear layers, which is capable of breaking masking schemes with uniformly distributed random masks.In addition, a novel scalable collision attack of general applicability and high-efficiency is proposed and applied to masked linear layers and masked S-boxes.It can reach an equal level of performance compared to the second-order power analysis with acceptable off-line search, which improves the known collision attacks significantly.
R-Trans: RNN Transformer Network for Chinese Machine Reading Comprehension

Machine reading comprehension (MRC) has gained increasingly wide attention over the past few years. A variety of benchmark datasets have been released, which triggers the development of quite a few MRC approaches based on deep learning techniques. However, most existing models are designed to address English MRC. When applying them directly to Chinese documents, the performance often degrades considerably because of some special characteristics of Chinese, the inevitable segmentation errors in particular. In this paper, we present the RNN Transformer network to tackle the Chinese MRC task. To mitigate the influence of incorrect word segmentation and mine sequential information of whole sentences, deep contextualized word representations and bidirectional gated recurrent units networks are adopted in our model. The extensive experiments have been conducted on a very large scale Chinese MRC corpus, viz., the Les MMRC dataset. The results show that the proposed model outperforms the baseline and other prevalent MRC models notably, and established a new state-of-the-art record on the Les MMRC dataset.
For What Technology Can’t Fix: Building a Model of Organizational Cybersecurity Culture

Organizational cybersecurity requires more than just the latest technology.To secure an organization, all members of the organization must act to reduce risk.Leaders have a special responsibility to understand, shape and align the beliefs, values, and attitudes of the entire organization with overall security goals.Managers need practical solutions for dealing with the human side of cybersecurity.The model presented in this paper describes organizational cybersecurity culture, the factors that contribute to its creation, and how it can be measured.A case study of a "culture of data protection" created by leaders at financial services firm Liberty Mutual illustrates these factors to help managers understand and apply recommendations to create a more mature cyber security culture in their organization.
Unexpected Inferences from Sensor Data: A Hidden Privacy Threat in the Internet of Things

Abstract A growing number of sensors, embedded in wearables, smart electric meters and other connected devices, is surrounding us and reaching ever deeper into our private lives. While some sensors are commonly regarded as privacy-sensitive and always require user permission to be activated, others are less protected and less worried about. However, experimental research findings indicate that many seemingly innocuous sensors can be exploited to infer highly sensitive information about people in their vicinity. This paper reviews existing evidence from the literature and discusses potential implications for consumer privacy. Specifically, the analysis reveals that certain insufficiently protected sensors in smart devices allow inferences about users’ locations, activities and real identities, as well as about their keyboard and touchscreen inputs. The presented findings call into question the adequacy of current sensor access policies. It is argued that most data captured by smart consumer devices should be classified as highly sensitive by default. An introductory overview of sensors commonly found in these devices is also provided, along with a proposed classification scheme.
Incorporating Word Attention into Character-Based Word Segmentation

Shohei Higashiyama, Masao Utiyama, Eiichiro Sumita, Masao Ideuchi, Yoshiaki Oida, Yohei Sakamoto, Isaac Okada. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation

Automatic pancreas segmentation in 3D radiological scans is a critical, yet challenging task. As a prerequisite for computer-aided diagnosis (CADx) systems, accurate pancreas segmentation could generate both quantitative and qualitative information towards establishing the severity of a condition, and thus provide additional guidance for therapy planning. Since the pancreas is an organ of high inter-patient anatomical variability, previous segmentation approaches report lower quantitative accuracy scores in comparison to abdominal organs such as the liver or kidneys. This paper presents a novel approach for automatic pancreas segmentation in magnetic resonance imaging (MRI) and computer tomography (CT) scans. This method exploits 3D segmentation that, when coupled with geometrical and morphological characteristics of abdominal tissue, classifies distinct contours in tight pixel-range proximity as “pancreas” or “non-pancreas”. There are three main stages to this approach: (1) identify a major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; (2) perform 3D segmentation via continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; (3) eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, structure and connectivity between distinct contours. The proposed method is evaluated on a dataset containing 82 CT image volumes, achieving mean Dice Similarity coefficient (DSC) of 79.3 ± 4.4%. Two MRI datasets containing 216 and 132 image volumes are evaluated, achieving mean DSC 79.6 ± 5.7% and 81.6 ± 5.1% respectively. This approach is statistically stable, reflected by lower metrics in standard deviation in comparison to state-of-the-art approaches.
Textual Analysis for Online Reviews: A Polymerization Topic Sentiment Model

More and more e-commerce companies realize the importance of analyzing the online reviews of their products.It is believed that online review has a significant impact on the shaping product brand and sales promotion.In this paper, we proposed a polymerization topic sentiment model (PTSM) to conduct textual analysis for online reviews.We applied this model to extract and filter the sentiment information from online reviews.Through integrating this model with machine learning methods, the results showed that the prediction accuracy had improved.Also, the experimental results showed that filtering sentiment topics hidden in the reviews are more important in influencing sales prediction, and the PTSM is more precise than other methods.The findings of this paper contribute to the knowledge that filtering the sentiment topics of online reviews could improve the prediction accuracy.Also, it could be applied by e-commerce practitioners as a new technique to conduct analyses of online reviews.
Crime Prediction Model using Deep Neural Networks

This project investigates the feasibility of using machine learning techniques, specifically neural networks, to make prediction on criminal behavior based on the history of the arrest bookings. The experiment will have to handle imbalanced data frequencies. To combat the challenge, data augmentation and weighted loss function is being developed to extract information from the minority classes. For this project, we have focused on how neural networks can be advantageous in classification of crime prediction. The specific kind of neural network that has been used in the project is a deep fully connected neural network. Fully connected neural networks are suitable for problems where domain knowledge is limited and many to many relations between features are important. As this report shows, machine learning techniques could definitely be of use for classification of criminal behavior, and we recommend exploring the discussed data augmentation and modeling methods more thoroughly to improve on the results and find new patterns.
Verifier-on-a-Leash: New Schemes for Verifiable Delegated Quantum Computation, with Quasilinear Resources

The problem of reliably certifying the outcome of a computation performed by a quantum device is rapidly gaining relevance. We present two protocols for a classical verifier to verifiably delegate a quantum computation to two non-communicating but entangled quantum provers. Our protocols have near-optimal complexity in terms of the total resources employed by the verifier and the honest provers, with the total number of operations of each party, including the number of entangled pairs of qubits required of the honest provers, scaling as $$O(g\log g)$$ for delegating a circuit of size g. This is in contrast to previous protocols, whose overhead in terms of resources employed, while polynomial, is far beyond what is feasible in practice. Our first protocol requires a number of rounds that is linear in the depth of the circuit being delegated, and is blind, meaning neither prover can learn the circuit or its input. The second protocol is not blind, but requires only a constant number of rounds of interaction. Our main technical innovation is an efficient rigidity theorem which allows a verifier to test that two entangled provers perform measurements specified by an arbitrary m-qubit tensor product of single-qubit Clifford observables on their respective halves of m shared EPR pairs, with a robustness that is independent of m. Our two-prover classical-verifier delegation protocols are obtained by combining this rigidity theorem with a single-prover quantum-verifier protocol for the verifiable delegation of a quantum computation, introduced by Broadbent.
An adaptive and on-line IMU-based locomotion activity classification method using a triplet Markov model

Detecting locomotion activities are critical for the analysis of human daily activities. In this paper, an adaptive on-line classification method is proposed to detect four lower limb locomotion activities – walking, running, stair ascent and descent – from the signals of a unique wearable sensor. The method is based on a non-parametric triplet Markov model, to detect gait phases and activities simultaneously, in an unsupervised way. This capability allows the model to work at run-time, and so to be used on-line. Also, an algorithm that adapts model parameters suits for a wide range of healthy human is presented. From this adjustment ability, an initial model can gradually approach to the dedicated activity patterns. Experimental results with ten healthy subjects show that our algorithm can reach an overall classification accuracy up to 99.20%, after the stabilization of parameters adjustment, regardless of the users’ gender, height, activity speed… Overall, the proposed algorithm presents a good performance in on-line parameters learning and high accuracy in classifying lower limb locomotion activities from a fount-mounted inertial measurement unit-based wearable sensor.
Autonomous Multi-Sensory Robotic Assistant for a Drinking Task

Assistive robots have the potential to support people with disabilities in their Activities of Daily Life. The drinking task has a high priority and requires constant assistance by caregivers to be executed regularly. Due to incapacitating disabilities such as tetraplegia, which is the paralysis of all limbs, affected people cannot use classic control interfaces such as joysticks. This paper presents a robotic solution to enable independent, straw-less drinking using a smart cup and no physically attached elements on the user. The system's hardware and software components are presented and the overarching control scheme described. The cup approaches the mouth utilising a user-friendly and vision-based robot control based on head pose estimation. Once contact has been established, the user can drink by tilting the cup with a force sensor-based control setup. Two experimental studies have been conducted, where the participants (mostly able-bodied and one tetraplegic), could separately experience the cup's contactless approach and the contact-based sequence. First results show a high user acceptance rate and consistent positive feedback. The evaluation of internal data showed a high reliability of the safety-critical components with the test groups perceiving the system as intuitive and easy to use.
Where Are You Taking Me? Behavioral Analysis of Open DNS Resolvers

Open DNS resolvers are resolvers that perform recursive resolution on behalf of any user. They can be exploited by adversaries because they are open to the public and require no authorization to use. Therefore, it is important to understand the state of open resolvers to gauge their potentially negative impact on the security and stability of the Internet. In this study, we conducted a comprehensive probing over the entire IPv4 address space and found that more than 3 million open resolvers still exist in the wild. Moreover, we found that many of them work in a way that deviates from the standard. More importantly, we found that many open resolvers answer queries with the incorrect, even malicious, responses. Contrasting to results obtained in 2013, we found that while the number of open resolvers has decreased significantly, the number of resolvers providing incorrect responses is almost the same, while the number of open resolvers providing malicious responses has increased, highlighting the prevalence of their threat.
Information Fusion for Multi-Source Material Data: Progress and Challenges

The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.