Web Service Reputation Evaluation Based on QoS Measurement

In the early service transactions, quality of service (QoS) information was published by service provider which was not always true and credible. For better verification the trust of the QoS information was provided by the Web service. In this paper, the factual QoS running data are collected by our WS-QoS measurement tool; based on these objectivity data, an algorithm compares the difference of the offered and measured quality data of the service and gives the similarity, and then a reputation evaluation method computes the reputation level of the Web service based on the similarity. The initial implementation and experiment with three Web services' example show that this approach is feasible and these values can act as the references for subsequent consumers to select the service

Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer

Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech during translation. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer between source and target speech. We propose an S2ST framework with an acoustic language model based on discrete units from a self-supervised model and a neural codec for style transfer. The acoustic language model leverages self-supervised in-context learning, acquiring the ability for style transfer without relying on any speaker-parallel data, thereby overcoming the issue of data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and style similarity. Audio samples are available at http://stylelm.github.io/ .Comment: 5 pages, 1 figure. submitted to ICASSP 202

Interplay between multiple charge-density waves and the relationship with superconductivity in Pdx_xHoTe3_{3}

HoTe$_{3}$, a member of the rare-earth tritelluride ($R$Te$_{3}$) family, and its Pd-intercalated compounds, Pd$_x$HoTe$_{3}$, where superconductivity (SC) sets in as the charge-density wave (CDW) transition is suppressed by the intercalation of a small amount of Pd, are investigated using angle-resolved photoemission spectroscopy (ARPES) and electrical resistivity. Two incommensurate CDWs with perpendicular nesting vectors are observed in HoTe$_{3}$ at low temperatures. With a slight Pd intercalation ($x$ = 0.01), the large CDW gap decreases and the small one increases. The momentum dependence of the gaps along the inner Fermi surface (FS) evolves from orthorhombicity to near tetragonality, manifesting the competition between two CDW orders. At $x$ = 0.02, both CDW gaps decreases with the emergence of SC. Further increasing the content of Pd for $x$ = 0.04 will completely suppress the CDW instabilities and give rise to the maximal SC order. The evolution of the electronic structures and electron-phonon couplings (EPCs) of the multiple CDWs upon Pd intercalation are carefully scrutinized. We discuss the interplay between multiple CDW orders, and the competition between CDW and SC in detail.Comment: 6 pages, 5 figure

Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation

As location-based services (LBS) have grown in popularity, the collection of human mobility data has become increasingly extensive to build machine learning (ML) models offering enhanced convenience to LBS users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges.Comment: 26 page

Preparation and characterization of a novel triple composite scaffold containing silk fibroin, chitosan, extracellular matrix and the mechanism of Akt/FoxO signaling pathway in colonic cancer cells cultured in 3D

This work examined the physical and chemical properties and biocompatibility in vivo and in vitro of a unique triple composite scaffold incorporating silk fibroin, chitosan, and extracellular matrix. The materials were blended, cross-linked, and freeze-dried to create a composite scaffold of silk fibroin/chitosan/colon extracellular matrix (SF/CTS/CEM) with varying CEM contents. The SF/CTS/CEM (1:1:1) scaffold demonstrated the preferable shape, outstanding porosity, favorable connectivity, good moisture absorption, and acceptable and controlled swelling and degradation properties. Additionally, HCT-116 cells cultivated with SF/CTS/CEM (1:1:1) showed excellent proliferation capacity, cell malignancy, and delayed apoptosis, according to the in vitro cytocompatibility examination. We also examined the PI3K/PDK1/Akt/FoxO signaling pathway and discovered that cell culture using a SF/CTS/CEM (1:1:1) scaffold may prevent cell death by phosphorylating Akt and suppressing FoxO expression. Our findings demonstrate the potential of the SF/CTS/CEM (1:1:1) scaffold as an experimental model for colonic cancer cell culture and for replicating the three-dimensional in vivo cell growth environment