Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Privacy and Security of Deep Learning Systems: A Survey

Deep learning has gained tremendous success and great popularity in the past few years. However, recent research found that it is suffering several inherent weaknesses, which can threaten the security and privacy of the stackholders. Deep learning's wide use further magnifies the caused consequences. To this end, lots of research has been conducted with the purpose of exhaustively identifying intrinsic weaknesses and subsequently proposing feasible mitigation. Yet few is clear about how these weaknesses are incurred and how effective are these attack approaches in assaulting deep learning. In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we are devoted to undertaking a comprehensive investigation on attacks towards deep learning, and extensively evaluating these attacks in multiple views. In particular, we focus on four types of attacks associated with security and privacy of deep learning: model extraction attack, model inversion attack, poisoning attack and adversarial attack. For each type of attack, we construct its essential workflow as well as adversary capabilities and attack goals. Many pivot metrics are devised for evaluating the attack approaches, by which we perform a quantitative and qualitative analysis. From the analysis, we have identified significant and indispensable factors in an attack vector, \eg, how to reduce queries to target models, what distance used for measuring perturbation. We spot light on 17 findings covering these approaches' merits and demerits, success probability, deployment complexity and prospects. Moreover, we discuss other potential security weaknesses and possible mitigation which can inspire relevant researchers in this area.
Towards Efficient Superconducting Quantum Processor Architecture Design

More computational resources (i.e., more physical qubits and qubit connections) on a superconducting quantum processor not only improve the performance but also result in more complex chip architecture with lower yield rate. Optimizing both of them simultaneously is a difficult problem due to their intrinsic trade-off. Inspired by the application-specific design principle, this paper proposes an automatic design flow to generate simplified superconducting quantum processor architecture with negligible performance loss for different quantum programs. Our architecture-design-oriented profiling method identifies program components and patterns critical to both the performance and the yield rate. A follow-up hardware design flow decomposes the complicated design procedure into three subroutines, each of which focuses on different hardware components and cooperates with corresponding profiling results and physical constraints. Experimental results show that our design methodology could outperform IBM's general-purpose design schemes with better Pareto-optimal results. ,0
Deep Learning to Scale up Time Series Traffic Prediction

The transport literature is dense regarding short-term traffic predictions, up to the scale of 1 hour, yet less dense for long-term traffic predictions. The transport literature is also sparse when it comes to city-scale traffic predictions, mainly because of low data availability. The main question we try to answer in this work is to which extent the approaches used for short-term prediction at a link level can be scaled up for long-term prediction at a city scale. We investigate a city-scale traffic dataset with 14 weeks of speed observations collected every 15 minutes over 1098 segments in the hypercenter of Los Angeles, California. We look at a variety of state-of-the-art machine learning and deep learning predictors for link-based predictions, and investigate ways to make such predictors scale up for larger areas, with brute force, clustering, and model design approaches. In particular we propose a novel deep learning spatio-temporal predictor inspired from recent works on recommender systems. We discuss the potential of including spatio-temporal features into the predictors, and conclude that modelling such features can be helpful for long-term predictions, while simpler predictors achieve very satisfactory performance for link-based and short-term forecasting. The trade-off is discussed not only in terms of prediction accuracy vs prediction horizon but also in terms of training time and model sizing.
Refining HTN Methods via Task Insertion with Preferences

Hierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to specify all HTN methods, leaving them incomplete. On the other hand, traditional HTN learning approaches focus only on declarative goals, omitting the hierarchical domain knowledge. In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. As it is difficult to identify incomplete methods without designating declarative goals for compound tasks, we introduce the notion of prioritized preference to capture the incompleteness possibility of methods. Specifically, the framework first computes the preferred completion profile w.r. t. the prioritized preference to refine the incomplete methods. Then it finds the minimal set of refined methods via a method substitution operation. Experimental analysis demonstrates that our approach is effective, especially in solving new HTN planning instances.
Online Pricing with Reserve Price Constraint for Personal Data Markets

society’s insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize her cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization, and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, our pricing mechanism provides a worst-case regret logarithmic in the number of queries. We further extend to other similar application scenarios, including hospitality service and online advertising, and extensively evaluate all three application instances over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.
Self-Supervised Learning by Cross-Modal Audio-Video Clustering

Visual and audio modalities are highly correlated, yet they contain different information. Their strong correlation makes it possible to predict the semantics of one from the other with good accuracy. Their intrinsic differences make cross-modal prediction a potentially more rewarding pretext task for self-supervised learning of video and audio representations compared to within-modality learning. Based on this intuition, we propose Cross-Modal Deep Clustering (XDC), a novel self-supervised method that leverages unsupervised clustering in one modality (e.g., audio) as a supervisory signal for the other modality (e.g., video). This cross-modal supervision helps XDC utilize the semantic correlation and the differences between the two modalities. Our experiments show that XDC outperforms single-modality clustering and other multi-modal variants. XDC achieves state-of-the-art accuracy among self-supervised methods on multiple video and audio benchmarks. Most importantly, our video model pretrained on large-scale unlabeled data significantly outperforms the same model pretrained with full-supervision on ImageNet and Kinetics for action recognition on HMDB51 and UCF101. To the best of our knowledge, XDC is the first self-supervised learning method that outperforms large-scale fully-supervised pretraining for action recognition on the same architecture.
Qualitative feed restriction for heavy swines: effect on digestibility and weight of organs of digestive tract, and environmental impact of feces

A total of 50 barrows were used to evaluate qualitative feed restriction on digestibility of dietary components, weight of organs of digestive tract, and composition and production of feces. Five experimental diets, with increasing levels of qualitative feed restriction of 0, 5, 10, 15, and 20% were used. There was linear reduction (P<0.001) of all digestibility coefficients on the levels of qualitative feed restriction, except for acid detergent fiber, which presented a quadratic response (P<0.05). Contents of total solids (P<0.01), volatile solids (P<0.05), and total minerals (P<0.001) in the feces augmented with increasing levels of qualitative feed restriction, whereas the levels of K (P<0.05), Cu (P<0.01), N, P, Na, Ca, Mg, Fe, and Zn (P<0.001) presented a quadratic response. Daily feces excretion; and contents of total solids, volatile solids, total minerals, and levels of N, P, K, Mn, and Cu (P<0.001) and Ca, Na, Mg, and Fe (P<0.05) presented a clear increase in function of the qualitative feed restriction level. Qualitative feed restriction can be an alternative for the disposal of agribusiness waste, providing good crop-fertilizing by the use of swine feces.
Reproductive cycle of the leopard grouper Mycteroperca rosacea in La Paz Bay, Mexico

The leopard grouper Mycteroperca rosacea is an endemic species from the northwestern coast of Mexico and, like other serranid fishes, it has a high commercial value; hence, it is a good candidate for cultivation. This study aimed to describe the reproductive cycle of the leopard grouper in the wild as a step toward evaluating its aquaculture and restocking potential. From March 2008 to February 2009, 197 specimens were collected in the Gulf of California, Mexico. Overall sex ratio was 3.6 females to 1.0 male. Significant differences (P < 0.05) in total length and body weight were found between sexes. Group-synchronous ovarian development was observed with one yearly spawning period from May to June. Males matured two months earlier than females. The reproductive cycle was divided into four periods: (1) maturation, (2) spawning, (3) post-spawning, and (4) resting. Significant variations in the gonadosomatic index, hepatosomatic index, visceral fat index, and condition factor were observed throughout the study period. In addition, nine individuals were classified as immature bisexuals and one grouper presented evidence of sex change. La cabrilla sardinera Mycteroperca rosacea es una especie endemica de la costa del noroeste de Mexico y, al igual que otras especies de serranidos, tiene un alto valor comercial; por lo tanto, es una buena candidata para cultivo. El objetivo de este estudio fue describir el ciclo reproductivo de la cabrilla sardinera en el medio natural como un paso hacia la evaluacion de su potencial acuicola y repoblamiento. De marzo de 2008 a febrero de 2009, 197 especimenes fueron recolectados en el golfo de California, Mexico. La proporcion de sexos total fue de 3.6 hembras a 1.0 macho. Se encontraron diferencias significativas (P < 0.05) en la longitud total y el peso corporal entre los sexos. Se observo un desarrollo ovarico del tipo sincronico por grupo con un periodo de puesta anual de mayo a junio. Los machos maduraron dos meses antes que las hembras. Se describieron cuatro periodos dentro del ciclo reproductivo: (1) maduracion, (2) desove, (3) posdesove y (4) reposo. Se observaron diferencias significativas en el indice gonadosomatico, el indice hepatosomatico, el indice de grasa visceral y el factor de condicion durante todo el periodo de estudio. Ademas, se clasificaron a nueve individuos como bisexuales inmaduros y se encontro una cabrilla con evidencia de cambio de sexo.
Mitoxantrone resistance in HL-60 leukemia cells: reduced nuclear topoisomerase II catalytic activity and drug-induced DNA cleavage in association with reduced expression of the topoisomerase II beta isoform.

Mitoxantrone-resistant variants of the human HL-60 leukemia cell line are cross-resistant to several natural product and synthetic antineoplastic agents. The resistant cells (HL-60/MX2) retain sensitivity to the Vinca alkaloids vincristine and vinblastine, drugs that are typically associated with the classical multidrug resistance phenotype. Mitoxantrone accumulation and retention are equivalent in the sensitive and resistant cell types, suggesting that mitoxantrone resistance in HL-60/MX2 cells might be associated with an alteration in the type II DNA topoisomerases. We discovered that topoisomerase II catalytic activity in 1.0 M NaCl nuclear extracts from the HL-60/MX2 variant, as measured by the decatenation of Crithidia fasciculata kinetoplast DNA, was reduced 4- to 5-fold compared to that in the parental HL-60 cells. Total cellular topoisomerase II activity in HL-60/MX2 cells was only 50% lower than that in HL-60 cells, however, because the "cytosolic fraction" of the HL-60/MX2 nuclear preparation contained high levels of decatenating activity. Antisera to calf thymus topoisomerase II defined a distinctive immunoreactive pattern of topoisomerase II proteins in crude nuclear extracts from the HL-60/MX2 cells. Both alpha (170 kDa) and beta (180 kDa) forms of topoisomerase II were detected in the HL-60 cell extracts, but only the alpha form was detected in extracts from HL-60/MX2 cells. This finding was associated with the appearance of a new 160-kDa immunoreactive species in nuclear extracts from HL-60/MX2 but not HL-60 cells. Studies were designed to minimize the proteolytic degradation of the topoisomerase II enzymes by extraction of whole cells with hot SDS. (ABSTRACT TRUNCATED AT 250 WORDS)
Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006

Shifts in the timing of spring phenology are a central feature of global change research. Long‐term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start‐of‐spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground‐based phenology, and two temperature‐driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day‐of‐year estimates by ±60 days and in standard deviation by ±20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground‐ or model‐based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring.
Mitigating effect of salicylic acid in the anatomy of the leaf of Zea mays L. lluteño ecotype from the Lluta Valley (Arica-Chile) under NaCl

Se sabe que la aplicacion del acido exogeno salicilico (SA) desempena un papel en la respuesta de las plantas a la salinidad y al estres osmotico. En anos recientes se ha demostrado que el SA aumenta las concentraciones de solutos organicos para la osmorregulacion en las plantas sometidas a estres salino. Por lo tanto, se realizo un experimento para investigar el impacto de factores exogenos como el SA en la morfologia de los tejidos de maiz lluteno. El tratamiento consistio en NaCl y SA (0,1 mM). El estres salino afecta negativamente la anatomia de la hoja, pero mostro un efecto positivo de SA en la composicion de estructuras tales como los vasos del xilema, la anatomia Kranz, celulas mesofilicas, tejidos epidermicos, laminas foliares y el desarrollo del cloroplasto. Tambien aumento el numero de estomas por unidad de area. Finalmente, la presencia de acido salicilico en condiciones salinas tiene efectos favorables sobre la morfologia de maiz lluteno, probablemente contribuyendo a la tolerancia a la salinidad.
Performance-Enhanced Non-Enzymatic Glucose Sensor Based on Graphene-Heterostructure

Non-enzymatic glucose sensing is a crucial field of study because of the current market demand. This study proposes a novel design of glucose sensor with enhanced selectivity and sensitivity by using graphene Schottky diodes, which is composed of graphene (G)/platinum oxide (PtO)/n-silicon (Si) heterostructure. The sensor was tested with different glucose concentrations and interfering solutions to investigate its sensitivity and selectivity. Different structures of the device were studied by adjusting the platinum oxide film thickness to investigate its catalytic activity. It was found that the film thickness plays a significant role in the efficiency of glucose oxidation and hence in overall device sensitivity. 0.8–2 μA output current was obtained in the case of 4–10 mM with a sensitivity of 0.2 μA/mM.cm2. Besides, results have shown that 0.8 μA and 15 μA were obtained by testing 4 mM glucose on two different PtO thicknesses, 30 nm and 50 nm, respectively. The sensitivity of the device was enhanced by 150% (i.e., up to 30 μA/mM.cm2) by increasing the PtO layer thickness. This was attributed to both the increase of the number of active sites for glucose oxidation as well as the increase in the graphene layer thickness, which leads to enhanced charge carriers concentration and mobility. Moreover, theoretical investigations were conducted using the density function theory (DFT) to understand the detection method and the origins of selectivity better. The working principle of the sensors puts it in a competitive position with other non-enzymatic glucose sensors. DFT calculations provided a qualitative explanation of the charge distribution across the graphene sheet within a system of a platinum substrate with D-glucose molecules above. The proposed G/PtO/n-Si heterostructure has proven to satisfy these factors, which opens the door for further developments of more reliable non-enzymatic glucometers for continuous glucose monitoring systems.
Product Knowledge Graph Embedding for E-commerce

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The ¶oincare embedding is also employed to handle complex entity structures. We use a real-world dataset from \textslgrocery.walmart. com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks.
DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling

For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep token representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods. DeFINE can be incorporated easily in new or existing sequence models. Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity. On WikiText-103, DeFINE reduces the total parameters of Transformer-XL by half with minimal impact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters. For machine translation, DeFINE improves the efficiency of the Transformer model by about 1.4 times while delivering similar performance.
Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time‐consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses

The Tsetlin Machine (TM) is an interpretable mechanism for pattern recognition that constructs conjunctive clauses from data. The clauses capture frequent patterns with high discriminating power, providing increasing expression power with each additional clause. However, the resulting accuracy gain comes at the cost of linear growth in computation time and memory usage. In this paper, we present the Weighted Tsetlin Machine (WTM), which reduces computation time and memory usage by weighting the clauses. Real-valued weighting allows one clause to replace multiple, and supports fine-tuning the impact of each clause. Our novel scheme simultaneously learns both the composition of the clauses and their weights. Furthermore, we increase training efficiency by replacing $k$ Bernoulli trials of success probability $p$ with a uniform sample of average size $p k$, the size drawn from a binomial distribution. In our empirical evaluation, the WTM achieved the same accuracy as the TM on MNIST, IMDb, and Connect-4, requiring only $1/4$, $1/3$, and $1/50$ of the clauses, respectively. With the same number of clauses, the WTM outperformed the TM, obtaining peak test accuracies of respectively $98.63\%$, $90.37\%$, and $87.91\%$. Finally, our novel sampling scheme reduced sample generation time by a factor of $7$.