Reviewerly/exHarmony huggingface dataset:
BRS cS: a hybrid recommendation model fusing multi-source heterogeneous data

Abstract Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendations, and fusing different types of data can make recommendations more accurate. Most existing fusion recommendation models simply combine the recommendation results from different data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users’ choices are usually affected by their direct and even indirect friends’ preferences. This paper proposes a hybrid recommendation model BRS c S (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top- N recommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRS c S algorithm proposed outperforms other recommendation algorithms such as BRS c , UserCF, and HRS c . The BRS c S model is also scalable and can fuse new types of data easily.
RELOAD+REFRESH: Abusing Cache Replacement Policies to Perform Stealthy Cache Attacks.

Caches have become the prime method for unintended information extraction across logical isolation boundaries. Even Spectre and Meltdown rely on the cache side channel, as it provides great resolution and is widely available on all major CPU platforms. As a consequence, several methods to stop cache attacks by detecting them have been proposed. Detection is strongly aided by the fact that observing cache activity of co-resident processes is not possible without altering the cache state and thereby forcing evictions on the observed processes. In this work, we show that this widely held assumption is incorrect. Through clever usage of the cache replacement policy it is possible to track a victims process cache accesses without forcing evictions on the victim's data. Hence, online detection mechanisms that rely on these evictions can be circumvented as they do not detect be the introduced RELOAD+REFRESH attack. The attack requires a profound understanding of the cache replacement policy. We present a methodology to recover the replacement policy and apply it to the last five generations of Intel processors. We further show empirically that the performance of RELOAD+REFRESH on cryptographic implementations is comparable to that of other widely used cache attacks, while its detectability becomes extremely difficult, due to the negligible effect on the victims cache access pattern.
Automatic Keywords Extraction Based on Co-Occurrence and Semantic Relationships Between Words

Automatic keywords extraction is a method that extracts words or phrases from a document which can express the main idea of the document.In this paper, we propose an unsupervised keywords extraction framework for individual documents, which improves the keywords extraction from two aspects.In the step of candidate keywords selection, we use the methods of removing the stopwords, regular matching, and length filtering to reduce the number of candidate keywords, but improve the quality.In the step of scoring words, we use word co-occurrence, semantic relationships (WordNet, Word Embedding, Normalized Google Distance), and three ways to combine word co-occurrence and semantic relationships to measure the weight of edges in the graph model.In experiments, we use Precision, Recall, and F1-measure values as evaluation criteria to compare all keywords extraction methods we proposed with other strong baseline methods in two datasets.According to the results of experiments, methods under our proposed framework achieve good results.We verify that the methods of using both word co-occurrence and semantic relationships have a better effect on keywords extraction than using co-occurrence or semantic relationships only.At the same time, we also find that for the keywords extraction of individual documents, the method of using co-occurrence between words has a better effect than semantic relationships.
Kegiatan Literasi Awal Orang Tua pada Anak Usia Dini

This research was conducted to determine the initial literacy activities carried out by parents, family members, and caregivers at home. The research method used for this study is a systematic review that is implemented by capturing articles concerned in the research topic published between 2009–2019 through two electronic search engines, Science Direct and Sage Journals. Furthermore, article extractions were carried out to filter out articles that were relevant to the research question (n = 9) with five articles with mother-child participants, 1 article with caregiver-child participants, and three articles with mother or father-child participants. Data analysis in this study is done by narrative synthesis analysis. The results of this study show several activities, i.e., shared reading or book reading, grocery games, writing letters, spelling, drawing pictures, and writing spontaneously. The most popular activity is the shared reading or reading book, which has more impact on the type of book and parent involvement. Limitations and recommendations for future research are discussed in this paper.
The emerging roles of academic librarians in Kenya: apomediaries or infomediaries?

Purpose The main purpose of this study was to investigate the emerging roles of academic librarians in Kenya, with a view to determining whether they perform better as apomediaries or infomediaries. The specific objectives were to: examine the characteristics of the changing information universe in which academic librarians in Kenya currently operate; analyse the information-seeking behaviour of academic library users in the new information universe and examine the emerging roles of academic librarians in Kenya. Design/methodology/approach This study adopted a descriptive research design and used an online survey research technique to collect data from practising academic librarians. This study targeted senior librarians from all the 67 private and public universities in Kenya. One senior librarian per university was purposefully selected to fill the questionnaire owing to their experience and expertise. 33 out of the 67 senior librarians responded to the survey. The collected data were descriptively analysed using SPSS, and as per the study objectives. Findings This study established that most library users are digital independent and access library resources remotely. Nonetheless, many users still borrow and utilize print books despite the ubiquity of digital platforms. The findings revealed that academic librarians to a great extent now play the role of apomediaries, going beyond information giving to empowering their users. Practical implications There is need for academic librarians in Kenya to not only be aware of the characteristics of their current users but also to continuously develop professionally so as to be able to adequately cater for the needs of their clients. Originality/value This paper contributes to the scholarship on librarians' roles in Kenya by demonstrating that most of them are transitioning to the apomediary roles.
The Role of CRM Fog Computing on Innovation and Customer Service Quality: an Empirical Study

CRM fog computing and entrepreneurial, innovation, and service quality have been identified as an important driving force for job growth. The study aimed to identify the involvement of CRM fog computing on entrepreneurial, innovation, and service quality in the Jordanian telecom company. The authors employed a quantitative approach to identify the level of the Appling of CRM fog computing in the company. The study contained the findings of a questionnaire of 325 respondents (male and female employees). Multiple linear regression and One-Way ANOVA analyses were used to examine the data. The predictive model of CRM fog computing and entrepreneurial, innovation, and service quality was a significant impact. Implications for CRM fog computing and practice are entrepreneurial, innovation, and service quality are discussed. The researchers recommend building enthusiasm for creating fog CRM mist processing as a result of its job in client administrations improvement: to increase using the information provided by CRM system in decision making; to consider services quality more as it affects sales volume positively; to consider customer’s feedback, listen to their opinions about the services provided to enhance performance. Entrepreneurship and innovation are increasingly an indispensable portion of organizations nationally, regionally and globally, and considered as a powerful tool for revenue streams. The authors state that entrepreneurship uses innovation to expand the business scope and boost growth. Therefore, entrepreneurship and innovation are dynamic and holistic processes that are not confined to the initial stage of a new venture. The implementation of CRM Fog computing and entrepreneurial aids the organization's productivity because it enhances the product and services offered by several organizations that adopt its concept. It enhances services to customers due to its easy to access nature and as well. The authors conclude that CRM Fog computing improves the e-commerce of any organization or community that adopts its concept and applies it in all areas. Keywords: CRM fog computing, entrepreneurial, innovation, service quality, customer experience.
Construction of Virtual Video Scene and Its Visualization During Sports Training

This article studies the actual captured human motion data for human motion synthesis and style transfer, constructs a scene of motion virtual video, and attempts to directly generate human motion style video to establish a sports style transfer model that combines and self-encoding.The original human motion capture data mapped to the motion feature space for style transfer synthesis.The coding network used to map the high-dimensional motion capture data to the low-dimensional feature space, and the motion style transfer constraints established in the feature space, and the human body motion results after the style transfer obtained by decoding.This paper proposes a pixel-level human motion style transfer model based on conditional adversarial networks and uses convolution and convolution to establish two branch coding networks to extract the features of the input style video and content pictures.The decoding network decodes the combined two features and generates a human motion video data frame by frame.The Gram matrix establishes constraints on the encoding and decoding features, controls the movement style of the human body, and finally realizes the visualization of the movement process.The incremental learning method based on the cascade network can improve the high accuracy and achieve the posture measurement frequency of 200Hz.The research results provide a key foundation for improving the immersion sensation of sport visual and tactile interaction simulation.
Temporal-Range-Doppler Features Interpretation and Recognition of Hand Gestures Using mmW FMCW Radar Sensors

This paper introduced a comparative study of using deep neural networks in non-contact hand gesture recognition based on millimeter wave FMCW radar. Range-doppler maps are processed with a zero-filling strategy to boost the range and velocity information of gesture motions. Two optimal types of deep neural networks, 3D-CNN and CNN-LSTM are respectively constructed to reveal the temporal gesture motion signatures encoded in multiple adjacent radar chirps. With the proposed networks, the recognition accuracy of six popular hand gestures reach to 95%. Meanwhile, this letter further explores the performance of the proposed networks in the impacts of training data size on the recognition accuracy. The proposed methods can be applied in the recognition of minor finger motions, providing some preliminary experimental results compared with other baseline methods.
Virtual and Augmented Reality in Surgery

To provide an overview of the novel applications of Augmented Reality (AR) and Virtual Reality (VR) technologies within the realm of Surgery. This chapter will clarify the concepts associated with AR and VR in the context of surgery, and address implementation approaches of AR and VR in surgical pre-operative planning and surgical training. Some examples of state-of-the-art solutions and system architecture developed by the editors will be described and discussed.
A Novel Image-Encryption Scheme Based on a Non-Linear Cross-Coupled Hyperchaotic System with the Dynamic Correlation of Plaintext Pixels

Based on a logistic map and Feigenbaum map, we proposed a logistic Feigenbaum non-linear cross-coupled hyperchaotic map (LF-NCHM) model. Experimental verification showed that the system is a hyperchaotic system. Compared with the existing cross-coupled mapping, LF-NCHM demonstrated a wider hyperchaotic range, better ergodicity and richer dynamic behavior. A hyperchaotic sequence with the same number of image pixels was generated by LF-NCHM, and a novel image-encryption algorithm with permutation that is dynamically related to plaintext pixels was proposed. In the scrambling stage, the position of the first scrambled pixel was related to the sum of the plaintext pixel values, and the positions of the remaining scrambled pixels were related to the pixel values after the previous scrambling. The scrambling operation also had a certain diffusion effect. In the diffusion phase, using the same chaotic sequence as in the scrambling stage increased the usage rate of the hyperchaotic sequence and improved the calculation efficiency of the algorithm. A large number of experimental simulations and cryptanalyses were performed, and the results proved that the algorithm had outstanding security and extremely high encryption efficiency. In addition, LF-NCHM could effectively resist statistical analysis attacks, differential attacks and chosen-plaintext attacks.
Collaborative deep learning across multiple data centers

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data of different organizations to a centralized data center owing to the constraints of privacy regulations. It is very challenging to conduct the geo-distributed deep learning among data centers without the privacy leaks. Model averaging is a conventional choice for data parallelized training and can reduce the risk of privacy leaks, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate (CLR) and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method.
High-Resolution Satellite Stereo Matching by Object-Based Semiglobal Matching and Iterative Guided Edge-Preserving Filter

This letter presents an object-based stereo matching and content-based guided disparity map refinement for high-resolution satellite stereo images. The proposed method uses the superpixels for object-based cost filtering and then object-based semiglobal matching. In the proposed object-based stereo matching strategy, two improvements including homogeneity weight for cost filtering, and weighted cost aggregation by image objects are considered. After cost aggregation, the generated disparity map is computed using the winner takes all and then refined with a new iterative guided edge-preserving filter. The proposed method with Census cost function has been implemented on high-resolution satellite stereo images and then is compared with the LiDAR ground truth. Moreover, the proposed method is compared with five state-of-the-art stereo matching methods. The experimental results on along-track satellite stereo images from Pleiades and IKONOS images and cross-track multidate images from WV-III demonstrate that the proposed method significantly improves the result of stereo matching.
A Novel Method of Heart Failure Prediction Based on DPCNN-XGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients, then the XGBOOST algorithm was used to construct the prediction model of heart failure. An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018. The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3% and 31% compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.
Move Fast and Break Things: Silicon Valley and the Language of Entrepreneurial Leadership

Entrepreneurs have long been recognized for their critical role in the development of a vibrant and growing economy. As key “agents of innovation and creative destruction” researchers have differentiated between entrepreneurs and managers predominantly in terms of two factors: (1) personality and (2) social cognitive processes or the way they think. In this study, we explore a new way of looking at the differences between entrepreneurs and managers that may be more useful for practicing managers: their language usage or the way they talk. Using text analysis software, we compare the language from interviews with Silicon Valley entrepreneurs (n = 65 entrepreneurs) with the language from interviews with senior managers (n = 65 managers). As hypothesized, entrepreneurs used significantly more words that were focused on the importance of time, money, and teamwork than managers. Contrary to expectations, managers used more positive emotion words compared with entrepreneurs. We conclude with a discussion of the limitations and implications of our findings for management practice and future research with a particular focus on lessons learned for managers wishing to act like entrepreneurs within their own organizations as intrepreneurs.
Cryogenic Current Steering DAC With Mitigated Variability

This letter presents an 8-b differential current steering digital-to-analog converter (DAC) for the cryogenic front-end of future quantum computers. Recently published characterization of CMOS technology reveals the deterioration of transistor matching properties at cryogenic temperatures. The preliminary study of the current mismatch in the FDSOI technology at 4.2 K allows proper sizing of the 255 current-source unit cells to mitigate nonlinearities and optimize the static DAC performance. Based on this study, we minimize the power consumption while maintaining the targeted nonlinearity, i.e., 7.3 μW for DNL = 0.64 LSB. With a 6.6-mV output range and 26-μV voltage step, our DAC is compatible with the foreseen requirements for line-grain biasing a Si-based qubit matrix at an expected 100-MS/s on-chip output drive.
Maximum Causal Entropy Specification Inference from Demonstrations

In many settings, such as robotics, demonstrations provide a natural way to specify tasks. However, most methods for learning from demonstrations either do not provide guarantees that the learned artifacts can be safely composed or do not explicitly capture temporal properties. Motivated by this deficit, recent works have proposed learning Boolean task specifications, a class of Boolean non-Markovian rewards which admit well-defined composition and explicitly handle historical dependencies. This work continues this line of research by adapting maximum causal entropy inverse reinforcement learning to estimate the posteriori probability of a specification given a multi-set of demonstrations. The key algorithmic insight is to leverage the extensive literature and tooling on reduced ordered binary decision diagrams to efficiently encode a time unrolled Markov Decision Process. This enables transforming a naïve algorithm with running time exponential in the episode length, into a polynomial time algorithm.
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process

We consider networks, trained via stochastic gradient descent to minimize $\ell_2$ loss, with the training labels perturbed by independent noise at each iteration. We characterize the behavior of the training dynamics near any parameter vector that achieves zero training error, in terms of an implicit regularization term corresponding to the sum over the data points, of the squared $\ell_2$ norm of the gradient of the model with respect to the parameter vector, evaluated at each data point. This holds for networks of any connectivity, width, depth, and choice of activation function. We interpret this implicit regularization term for three settings: matrix sensing, two layer ReLU networks trained on one-dimensional data, and two layer networks with sigmoid activations trained on a single datapoint. For these settings, we show why this new and general implicit regularization effect drives the networks towards simple models.
Impact of Four-Wave-Mixing Noise from Dense Wavelength-Division-Multiplexing Systems on Entangled-State Continuous-Variable Quantum key Distribution

Integrating quantum key distribution (QKD) on the existing optical fiber network through dense wavelength-division multiplexing (DWDM) can greatly reduce the costs of quantum channels and improve the scalability and flexibility of the quantum communication network. In the scenarios of an access network or local area network with a large number of users, the connected optical fibers are relatively short and the quantum signal can be adjacent to the classical channels. In this case, the four-wave-mixing noise generated by the classical lights will have a severe impact on the performance of the QKD. We establish a theoretical model to characterize the excess noise in continuous-variable QKD induced by the four-wave mixing, and experimentally demonstrate the validity of the theoretical predictions. In addition, the influence of cross-phase-modulation noise between the carrier of the quantum signal and classical data channels on the QKD is investigated. To suppress the effect of the four-wave-mixing noise, an unequal channel spacing technique is employed. We realize coexistence of entangled state-based continuous-variable QKD and five adjacent DWDM classical channels with 10 dBm launch power and nonreturn-to-zero on-off-keying modulation at 2.5 Gb/s (10 Gb/s).