LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.

Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users’ characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.

Steady-state thermodynamics for population dynamics in fluctuating environments with side information

Steady-state thermodynamics (SST) is a relatively newly emerging subfield of physics, which deals with transitions between steady states. In this paper, we find an SST-like structure in population dynamics of organisms that can sense their fluctuating environments. As heat is divided into two parts in SST, we decompose population growth into two parts: housekeeping growth and excess growth. Then, we derive the Clausius equality and inequality for excess growth. Using numerical simulations, we demonstrate how the Clausius inequality behaves depending on the magnitude of noise and strategies that organisms employ. Finally, we discuss the novelty of our findings and compare them with a previous study.

Robust Video Steganography for Social Media Sharing Based on Principal Component Analysis

Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication on social media channels. A new strategy based on principal component analysis is provided to select robust embedding regions. Besides, side information is generated to label these selected regions. Side information compression is designed to reduce the transmission bandwidth cost. Then, one luminance component and one chrominance component are joined to embed secret messages and side information, keeping the embedding and extraction positions in sync. Video preprocessing is conducted to improve the applicability of our proposed method to various video transcoding mechanisms. Experimental results have shown that our proposed method provides strong robustness against video transcoding and achieves satisfactory security performance against steganalysis. The bit error rate of our method is lower than state-of-the-art robust video steganographic methods. It is a robust and secure method to realize reliable hidden communication over social media channels, such as YouTube and Vimeo.

A Fit between Logistics and Supply Chain Management Education and Jobs Market Need in Nepal

The purpose of this paper is to explore a fit between Logistics and Supply Chain Management (LSCM) related course content and the industry needs in the Nepalese context. Since this study is undertaken using the Nepalese sample, the knowledge and skills prioritized by employers, it can be of value to educators while designing their LSCM course content. Desk-based research involving content analyses was done to understand the supply side information i.e. relative coverage of LSCM topics in business-related courses and degrees offered at selected five Nepalese Universities and for the demand side information i.e. analysis of job description of the LSCM related vacancies in the Nepalese job market over eighteen weeks. The study reveals that the inclusion of LSCM courses in the business programs at Nepalese Universities is currently negligible as none of them offered a separate program dedicated to LSCM. Besides, the LSCM courses were limited to just one course per program weighing not more than three credit hours. Instead, there seems to be an unprecedented number of business schools and colleges leaning towards more sellable traditional business degrees related to finance, marketing, human resource management, IT, and hospitality. Finally, when comparing the relative coverage of LSCM topics in the curriculum to the relative demand for such knowledge by the employers, there seems to be an over-emphasis or under-emphasis of courses related to LSCM both at the bachelors and masters level indicating a mismatch between the expectations of employers and education offered by the universities.

Encoding Individual Source Sequences for the Wiretap Channel

We consider the problem of encoding a deterministic source sequence (i.e., individual sequence) for the degraded wiretap channel by means of an encoder and decoder that can both be implemented as finite-state machines. Our first main result is a necessary condition for both reliable and secure transmission in terms of the given source sequence, the bandwidth expansion factor, the secrecy capacity, the number of states of the encoder and the number of states of the decoder. Equivalently, this necessary condition can be presented as a converse bound (i.e., a lower bound) on the smallest achievable bandwidth expansion factor. The bound is asymptotically achievable by Lempel–Ziv compression followed by good channel coding for the wiretap channel. Given that the lower bound is saturated, we also derive a lower bound on the minimum necessary rate of purely random bits needed for local randomness at the encoder in order to meet the security constraint. This bound too is achieved by the same achievability scheme. Finally, we extend the main results to the case where the legitimate decoder has access to a side information sequence, which is another individual sequence that may be related to the source sequence, and a noisy version of the side information sequence leaks to the wiretapper.

UNIVERSAL RISK BUDGETING

I juxtapose Cover’s vaunted universal portfolio selection algorithm ([Cover, TM (1991). Universal portfolios. Mathematical Finance, 1, 1–29]) with the modern representation of a portfolio as a certain allocation of risk among the available assets, rather than a mere allocation of capital. Thus, I define a Universal Risk Budgeting scheme that weights each risk budget, instead of each capital budget, by its historical performance record, á la Cover. I prove that my scheme is mathematically equivalent to a novel type of [Cover, TM and E Ordentlich (1996). Universal portfolios with side information. IEEE Transactions on Information Theory, 42, 348–363] universal portfolio that uses a new family of prior densities that have hitherto not appeared in the literature on universal portfolio theory. I argue that my universal risk budget, so-defined, is a potentially more perspicuous and flexible type of universal portfolio; it allows the algorithmic trader to incorporate, with advantage, his prior knowledge or beliefs about the particular covariance structure of instantaneous asset returns. Say, if there is some dispersion in the volatilities of the available assets, then the uniform or Dirichlet priors that are standard in the literature will generate a dangerously lopsided prior distribution over the possible risk budgets. In the author’s opinion, the proposed “Garivaltis prior” makes for a nice improvement on Cover’s timeless expert system, that is properly agnostic and open to different risk budgets from the very get-go. Inspired by [Jamshidian, F (1992). Asymptotically optimal portfolios. Mathematical Finance, 2, 131–150], the universal risk budget is formulated as a new kind of exotic option in the continuous time Black–Scholes market, with all the pleasure, elegance, and convenience that entails.