nils-herrmann/clean_scirepeval huggingface dataset:
Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior

Modeling the complex interactions between users and items is at the core of designing successful recommender systems. One key task consists of predicting users’ personalized sequential behavior, where the challenge mainly lies in modeling ‘third-order’ interactions between a user, her previously visited item(s), and the next item to consume. In this paper, we propose a unified method, TransRec, to model such interactions for largescale sequential prediction. Methodologically, we embed items into a ‘transition space’ where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets.
Digital image steganography: Survey and analysis of current methods

Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various useful applications. However, like any other science it can be used for ill intentions. It has been propelled to the forefront of current security techniques by the remarkable growth in computational power, the increase in security awareness by, e.g., individuals, groups, agencies, government and through intellectual pursuit. Steganography’s ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature. This paper concludes with some recommendations and advocates for the object-oriented embedding mechanism. Steganalysis, which is the science of attacking steganography, is not the focus of this survey but nonetheless will be briefly discussed. & 2009 Elsevier B.V. All rights reserved.
Learning to Reconstruct High-Quality 3D Shapes with Cascaded Fully Convolutional Networks

We present a data-driven approach to reconstructing highresolution and detailed volumetric representations of 3D shapes. Although well studied, algorithms for volumetric fusion from multi-view depth scans are still prone to scanning noise and occlusions, making it hard to obtain high-fidelity 3D reconstructions. In this paper, inspired by recent advances in efficient 3D deep learning techniques, we introduce a novel cascaded 3D convolutional network architecture, which learns to reconstruct implicit surface representations from noisy and incomplete depth maps in a progressive, coarse-to-fine manner. To this end, we also develop an algorithm for end-to-end training of the proposed cascaded structure. Qualitative and quantitative experimental results on both simulated and real-world datasets demonstrate that the presented approach outperforms existing state-of-the-art work in terms of quality and fidelity of reconstructed models.
Wikipedia Based Short Text Classification Method

Short text is usually expressed in refined slightly, insufficient information, which makes text classification difficult. But we can try to introduce some information from the existing knowledge base to strengthen the performance of short text classification. Wikipedia [2,13,15] is now the largest human-edited knowledge base of high quality. It would benefit to short text classification if we can make full use of Wikipedia information in short text classification. This paper presents a new concept based [22] on Wikipedia short text representation method, by identifying the concept of Wikipedia mentioned in short text, and then expand the concept of wiki correlation and short text messages to the feature vector representation.
Encapsulation in the food industry: a review.

Encapsulation involves the incorporation of food ingredients, enzymes, cells or other materials in small capsules. Applications for this technique have increased in the food industry since the encapsulated materials can be protected from moisture, heat or other extreme conditions, thus enhancing their stability and maintaining viability. Encapsulation in foods is also utilized to mask odours or tastes. Various techniques are employed to form the capsules, including spray drying, spray chilling or spray cooling, extrusion coating, fluidized bed coating, liposome entrapment, coacervation, inclusion complexation, centrifugal extrusion and rotational suspension separation. Each of these techniques is discussed in this review. A wide variety of foods is encapsulated--flavouring agents, acids bases, artificial sweeteners, colourants, preservatives, leavening agents, antioxidants, agents with undesirable flavours, odours and nutrients, among others. The use of encapsulation for sweeteners such as aspartame and flavours in chewing gum is well known. Fats, starches, dextrins, alginates, protein and lipid materials can be employed as encapsulating materials. Various methods exist to release the ingredients from the capsules. Release can be site-specific, stage-specific or signalled by changes in pH, temperature, irradiation or osmotic shock. In the food industry, the most common method is by solvent-activated release. The addition of water to dry beverages or cake mixes is an example. Liposomes have been applied in cheese-making, and its use in the preparation of food emulsions such as spreads, margarine and mayonnaise is a developing area. Most recent developments include the encapsulation of foods in the areas of controlled release, carrier materials, preparation methods and sweetener immobilization. New markets are being developed and current research is underway to reduce the high production costs and lack of food-grade materials.
Learning Structured Semantic Embeddings for Visual Recognition

Numerous embedding models have been recently explored to incorporate semantic knowledge into visual recognition. Existing methods typically focus on minimizing the distance between the corresponding images and texts in the embedding space but do not explicitly optimize the underlying structure. Our key observation is that modeling the pairwise image-image relationship improves the discrimination ability of the embedding model. In this paper, we propose the structured discriminative and difference constraints to learn visual-semantic embeddings. First, we exploit the discriminative constraints to capture the intraand inter-class relationships of image embeddings. The discriminative constraints encourage separability for image instances of different classes. Second, we align the difference vector between a pair of image embeddings with that of the corresponding word embeddings. The difference constraints help regularize image embeddings to preserve the semantic relationships among word embeddings. Extensive evaluations demonstrate the effectiveness of the proposed structured embeddings for single-label classification, multilabel classification, and zero-shot recognition.
Consciousness: here, there and everywhere?

The science of consciousness has made great strides by focusing on the behavioural and neuronal correlates of experience. However, while such correlates are important for progress to occur, they are not enough if we are to understand even basic facts, for example, why the cerebral cortex gives rise to consciousness but the cerebellum does not, though it has even more neurons and appears to be just as complicated. Moreover, correlates are of little help in many instances where we would like to know if consciousness is present: patients with a few remaining islands of functioning cortex, preterm infants, non-mammalian species and machines that are rapidly outperforming people at driving, recognizing faces and objects, and answering difficult questions. To address these issues, we need not only more data but also a theory of consciousness-one that says what experience is and what type of physical systems can have it. Integrated information theory (IIT) does so by starting from experience itself via five phenomenological axioms: intrinsic existence, composition, information, integration and exclusion. From these it derives five postulates about the properties required of physical mechanisms to support consciousness. The theory provides a principled account of both the quantity and the quality of an individual experience (a quale), and a calculus to evaluate whether or not a particular physical system is conscious and of what. Moreover, IIT can explain a range of clinical and laboratory findings, makes a number of testable predictions and extrapolates to a number of problematic conditions. The theory holds that consciousness is a fundamental property possessed by physical systems having specific causal properties. It predicts that consciousness is graded, is common among biological organisms and can occur in some very simple systems. Conversely, it predicts that feed-forward networks, even complex ones, are not conscious, nor are aggregates such as groups of individuals or heaps of sand. Also, in sharp contrast to widespread functionalist beliefs, IIT implies that digital computers, even if their behaviour were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing.
Padova Smart City: An urban Internet of Things experimentation

“Smart City” is a powerful paradigm that applies the most advanced communication technologies to urban environments, with the final aim of enhancing the quality of life in cities and provide a wide set of value-added services to both citizens and administration. A fundamental step towards the practical realization of the Smart City concept consists in the development of a communication infrastructure capable of collecting data from a large variety of different devices in a mostly uniform and seamless manner, according to the Internet of Things (IoT) paradigm. While the scientific and commercial interest in IoT has been constantly growing in the last years, practical experimentation of IoT systems has just begun. In this paper, we present and discuss the Padova Smart City system, an experimental realization of an urban IoT system designed within the Smart City framework and deployed in the city of Padova, Italy. We describe the system architecture and discuss the fundamental technical choices at the base of the project. Then, we analyze the data collected by the system and show how simple data processing techniques can be used to gain insights on the functioning of the monitored system, public traffic lighting in our specific case, as well as other information concerning the urban environment.
Making it rain with cloud payment processing vulnerabilities

In this paper, we investigate the value of Payment Card Industry Data Security Standard (PCI DSS) by examining popular implementations of hosted payment processing solutions. First, we will dive into Card Connect, a registered ISO of Wells Fargo Bank, and see how their client-side-dependent model allows for trivial manipulation. Then we look at Card Connect plugins for more widespread vulnerabilities. Lastly, we propose a sort of leaky-bucket solution wherein the e-commerce platform must validate all of the assumptions previously made; albeit, tokenized credit card transactions are a technology that should be leveraged when resources allow. However, hiring a skilled software developer to implement a secure credit card processing system is out of reach for many small retail shops.
Deep neural network based malware detection using two dimensional binary program features

In this paper we introduce a deep neural network based malware detection system that Invincea has developed, which achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware. We show that our system achieves a 95% detection rate at 0.1% false positive rate (FPR), based on more than 400,000 software binaries sourced directly from our customers and internal malware databases. In addition, we describe a non-parametric method for adjusting the classifier's scores to better represent expected precision in the deployment environment. Our results demonstrate that it is now feasible to quickly train and deploy a low resource, highly accurate machine learning classification model, with false positive rates that approach traditional labor intensive expert rule based malware detection, while also detecting previously unseen malware missed by these traditional approaches. Since machine learning models tend to improve with larger datasizes, we foresee deep neural network classification models gaining in importance as part of a layered network defense strategy in coming years.
Broadcast Disks: Data Management for Asymmetric Communications Environments

This paper proposes the use of repetitive broadcast as a way of augmenting the memory hierarchy of clients in an asymmetric communication environment. We describe a new technique called "Broadcast Disks" for structuring the broadcast in a way that provides improved performance for non-uniformly accessed data. The Broadcast Disk superimposes multiple disks spinning at different speeds on a single broadcast channel--in effect creating an arbitrarily fine-grained memory hierarchy. In addition to proposing and defining the mechanism, a main result of this work is that exploiting the potential of the broadcast structure requires a re-evaluation of basic cache management policies. We examine several "pure" cache management policies and develop and measure implementable approximations to these policies. These results and others are presented in a set of simulation studies that substantiates the basic idea and develops some of the intuitions required to design a particular broadcast program.
Physics-of-failure-based prognostics for electronic products

This paper presents a physics-of-failure (PoF)-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product’s life cycle loading and failure mechanisms to perform reliability design and assessment. PoF-based prognostics permit the assessment of product reliability under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition (ie, the product’s ‘health’) and the prediction of the future state of reliability. A formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life cycle loads, damage accumulation, and assessment of uncertainty, is presented. Then, applications of PoF-based prognostics are discussed.
Review and management of clozapine side effects.

Clozapine has demonstrated superior efficacy in relieving positive and negative symptoms in treatment-resistant schizophrenic patients; unlike other antipsychotics, it causes minimal extrapyramidal side effects (EPS) and has little effect on serum prolactin. Despite these benefits, the use of clozapine has been limited because of infrequent but serious side effects, the most notable being agranulocytosis. In recent years, however, mandatory blood monitoring has significantly reduced both the incidence of agranulocytosis and its associated mortality. The occurrence of seizures appears to be dose-related and can generally be managed by reduction in clozapine dosage. Less serious and more common side effects of clozapine including sedation, hypersalivation, tachycardia, hypotension, hypertension, weight gain, constipation, urinary incontinence, and fever can often be managed medically and are generally tolerated by the patient. Appropriate management of clozapine side effects facilitates a maximization of the benefits of clozapine treatment, and physicians and patients alike should be aware that there is a range of benefits to clozapine use that is wider than its risks.
Unsupervised Learning of Disentangled Representations from Video

We present a new model DRNET that learns disentangled image representations from video. Our approach leverages the temporal coherence of video and a novel adversarial loss to learn a representation that factorizes each frame into a stationary part and a temporally varying component. The disentangled representation can be used for a range of tasks. For example, applying a standard LSTM to the time-vary components enables prediction of future frames. We evaluate our approach on a range of synthetic and real videos, demonstrating the ability to coherently generate hundreds of steps into the future.
Car parking control using a trajectory tracking controller

One of the key technologies of future automobiles is the parking assist or automatic parking control. Control problems of a car-like vehicle are not easy because of nonholonomic velocity constraints. This paper proposes a parking control strategy which is composed of an open loop path planner and a feedback tracking controller. By employing a trajectory tracking controller for a 2 wheeled robot, a car-like vehicle can be successfully controlled to the desired configuration. Experimental results with a radio controlled model car clearly show that the proposed control scheme is practically useful
Adversarial domain separation and adaptation

Traditional domain adaptation methods attempted to learn the shared representation for distribution matching between source domain and target domain where the individual information in both domains was not characterized. Such a solution suffers from the mixing problem of individual information with the shared features which considerably constrains the performance for domain adaptation. To relax this constraint, it is crucial to extract both shared information and individual information. This study captures both information via a new domain separation network where the shared features are extracted and purified via separate modeling of individual information in both domains. In particular, a hybrid adversarial learning is incorporated in a separation network as well as an adaptation network where the associated discriminators are jointly trained for domain separation and adaptation according to the minmax optimization over separation loss and domain discrepancy, respectively. Experiments on different tasks show the merit of using the proposed adversarial domain separation and adaptation.
Current State of Text Sentiment Analysis from Opinion to Emotion Mining

Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.
Prenatal maternal stress: effects on pregnancy and the (unborn) child.

Animal experiments have convincingly demonstrated that prenatal maternal stress affects pregnancy outcome and results in early programming of brain functions with permanent changes in neuroendocrine regulation and behaviour in offspring.



To evaluate the existing evidence of comparable effects of prenatal stress on human pregnancy and child development.



Data sources used included a computerized literature search of PUBMED (1966-2001); Psychlit (1987-2001); and manual search of bibliographies of pertinent articles.



Recent well-controlled human studies indicate that pregnant women with high stress and anxiety levels are at increased risk for spontaneous abortion and preterm labour and for having a malformed or growth-retarded baby (reduced head circumference in particular). Evidence of long-term functional disorders after prenatal exposure to stress is limited, but retrospective studies and two prospective studies support the possibility of such effects. A comprehensive model of putative interrelationships between maternal, placental, and fetal factors is presented.



Apart from the well-known negative effects of biomedical risks, maternal psychological factors may significantly contribute to pregnancy complications and unfavourable development of the (unborn) child. These problems might be reduced by specific stress reduction in high anxious pregnant women, although much more research is needed.
Data center TCP (DCTCP)

Cloud data centers host diverse applications, mixing workloads that require small predictable latency with others requiring large sustained throughput. In this environment, today's state-of-the-art TCP protocol falls short. We present measurements of a 6000 server production cluster and reveal impairments that lead to high application latencies, rooted in TCP's demands on the limited buffer space available in data center switches. For example, bandwidth hungry "background" flows build up queues at the switches, and thus impact the performance of latency sensitive "foreground" traffic.
 To address these problems, we propose DCTCP, a TCP-like protocol for data center networks. DCTCP leverages Explicit Congestion Notification (ECN) in the network to provide multi-bit feedback to the end hosts. We evaluate DCTCP at 1 and 10Gbps speeds using commodity, shallow buffered switches. We find DCTCP delivers the same or better throughput than TCP, while using 90% less buffer space. Unlike TCP, DCTCP also provides high burst tolerance and low latency for short flows. In handling workloads derived from operational measurements, we found DCTCP enables the applications to handle 10X the current background traffic, without impacting foreground traffic. Further, a 10X increase in foreground traffic does not cause any timeouts, thus largely eliminating incast problems.
Generalized Value Iteration Networks: Life Beyond Lattices

In this paper, we introduce a generalized value iteration network (GVIN), which is an end-to-end neural network planning module. GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. We propose three novel differentiable kernels as graph convolution operators and show that the embedding-based kernel achieves the best performance. Furthermore, we present episodic Q-learning, an improvement upon traditional n-step Q-learning that stabilizes training for VIN and GVIN. Lastly, we evaluate GVIN on planning problems in 2D mazes, irregular graphs, and realworld street networks, showing that GVIN generalizes well for both arbitrary graphs and unseen graphs of larger scale and outperforms a naive generalization of VIN (discretizing a spatial graph into a 2D image).