nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating Recurrent Neural Networks: A Memory-Efficient Approach

Recurrent neural networks (RNNs) have achieved the state-of-the-art performance on various sequence learning tasks due to their powerful sequence modeling capability. However, RNNs usually require a large number of parameters and high computational complexity. Hence, it is quite challenging to implement complex RNNs on embedded devices with stringent memory and latency requirement. In this paper, we first present a novel hybrid compression method for a widely used RNN variant, long–short term memory (LSTM), to tackle these implementation challenges. By properly using circulant matrices, forward nonlinear function approximation, and efficient quantization schemes with a retrain-based training strategy, the proposed compression method can reduce more than 95% of memory usage with negligible accuracy loss when verified under language modeling and speech recognition tasks. An efficient scalable parallel hardware architecture is then proposed for the compressed LSTM. With an innovative chessboard division method for matrix–vector multiplications, the parallelism of the proposed hardware architecture can be freely chosen under certain latency requirement. Specifically, for the circulant matrix–vector multiplications employed in the compressed LSTM, the circulant matrices are judiciously reorganized to fit in with the chessboard division and minimize the number of memory accesses required for the matrix multiplications. The proposed architecture is modeled using register transfer language (RTL) and synthesized under the TSMC 90-nm CMOS technology. With 518.5-kB on-chip memory, we are able to process a  $512 \times 512$  compressed LSTM in 1.71  $\mu {{{\text{s}}}}$ , corresponding to 2.46 TOPS on the uncompressed one, at a cost of 30.77-mm2 chip area. The implementation results demonstrate that the proposed design can achieve significantly high flexibility and area efficiency, which satisfies many real-time applications on embedded devices. It is worth mentioning that the memory-efficient approach of accelerating LSTM developed in this paper is also applicable to other RNN variants.
Event Representations for Automated Story Generation with Deep Neural Nets

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a midlevel of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.
Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness

Mindfulness-as a state, trait, process, type of meditation, and intervention has proven to be beneficial across a diverse group of psychological disorders as well as for general stress reduction. Yet, there remains a lack of clarity in the operationalization of this construct, and underlying mechanisms. Here, we provide an integrative theoretical framework and systems-based neurobiological model that explains the mechanisms by which mindfulness reduces biases related to self-processing and creates a sustainable healthy mind. Mindfulness is described through systematic mental training that develops meta-awareness (self-awareness), an ability to effectively modulate one's behavior (self-regulation), and a positive relationship between self and other that transcends self-focused needs and increases prosocial characteristics (self-transcendence). This framework of self-awareness, -regulation, and -transcendence (S-ART) illustrates a method for becoming aware of the conditions that cause (and remove) distortions or biases. The development of S-ART through meditation is proposed to modulate self-specifying and narrative self-networks through an integrative fronto-parietal control network. Relevant perceptual, cognitive, emotional, and behavioral neuropsychological processes are highlighted as supporting mechanisms for S-ART, including intention and motivation, attention regulation, emotion regulation, extinction and reconsolidation, prosociality, non-attachment, and decentering. The S-ART framework and neurobiological model is based on our growing understanding of the mechanisms for neurocognition, empirical literature, and through dismantling the specific meditation practices thought to cultivate mindfulness. The proposed framework will inform future research in the contemplative sciences and target specific areas for development in the treatment of psychological disorders.
Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods

Historical data collected from processes are readily available. This paper looks at recent advances in the use of data-driven models built from such historical data for monitoring, fault diagnosis, optimization and control. Latent variable models are used because they provide reduced dimensional models for high dimensional processes. They also provide unique, interpretable and causal models, all of which are necessary for the diagnosis, control and optimization of any process. Multivariate latent variable monitoring and fault diagnosis methods are reviewed and contrasted with classical fault detection and diagnosis approaches. The integration of monitoring and diagnosis techniques by using an adaptive agent-based framework is outlined and its use for fault-tolerant control is compared with alternative fault-tolerant control frameworks. The concept of optimizing and controlling high dimensional systems by performing optimizations in the low dimensional latent variable spaces is presented and illustrated by means of several industrial examples.
Effectiveness of a Mindfulness-Based Intervention for Athletes

Recently, several top athletes, as well as coaches, have revealed that mindfulness can be regarded as a type of psychological training to optimize performance. Indeed, results of studies in clinical psychology, as well as neuroscience, suggest that mindfulness might be an effective strategy to promote concentration, emotion regulation and state of flow. However, the empirical evidence in the context of sport is rather sparse. The aim of this study was to examine the effectiveness of a mindfulness-based intervention in athletes. In a randomized controlled trial with 22 participants in the experimental and 24 participants in the control group, the effectiveness of the Berlin Mindfulness-based Training for Athletes (BATL) was tested and compared to a classical sport psychological intervention. The results of an analysis of covariance with repeated measures indicate that the intervention group significantly improved mindfulness as a trait compared to the control group. It can be concluded that BATL is an effective strategy to increase mindfulness in athletes. However, further studies are needed to evaluate the effectiveness of this intervention in improving performance and to be able to examine its effect mechanisms.
An Overview of Penetration Testing

Penetration testing is a series of activities undertaken to identify and exploit security vulnerabilities. It helps confirm the effectiveness or ineffectiveness of the security measures that have been implemented. This paper provides an overview of penetration testing. It discusses the benefits, the strategies and the methodology of conducting penetration testing. The methodology of penetration testing includes three phases: test preparation, test and test analysis. The test phase involves the following steps: information gathering, vulnerability analysis, and vulnerability exploit. This paper further illustrates how to apply this methodology to conduct penetration testing on two example web applications.
An open access database for the evaluation of heart sound algorithms.

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.
Improving palliative care with deep learning

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3–12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.
Automatic Photo Adjustment Using Deep Neural Networks

Photo retouching enables photographers to invoke dramatic visual impressions by artistically enhancing their photos through stylistic color and tone adjustments. However, it is also a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. Using an automated algorithm is an appealing alternative to manual work, but such an algorithm faces many hurdles. Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics. Further, these adjustments are often spatially varying. Existing automatic algorithms are still limited and cover only a subset of these challenges. Recently, deep learning has shown unique abilities to address hard problems. This motivated us to explore the use of deep neural networks (DNNs) in the context of photo editing. In this article, we formulate automatic photo adjustment in a manner suitable for this approach. We also introduce an image descriptor accounting for the local semantics of an image. Our experiments demonstrate that training DNNs using these descriptors successfully capture sophisticated photographic styles. In particular and unlike previous techniques, it can model local adjustments that depend on image semantics. We show that this yields results that are qualitatively and quantitatively better than previous work.
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

Quantum computers can (in theory) solve certain problems far faster than a classical computer running any known classical algorithm.While existing technologies for building quantum computers are in their infancy, it is not too early to consider their scalability and reliability in the context of the design of large-scale quantum computers. To architect such systems, one must understand what it takes to design and model a balanced, fault-tolerant quantum computer architecture. The goal of this lecture is to provide architectural abstractions for the design of a quantum computer and to explore the systems-level challenges in achieving scalable, fault-tolerant quantum computation. In this lecture,we provide an engineering-oriented introduction to quantum computation with an overview of the theory behind key quantum algorithms. Next, we look at architectural case studies based upon experimental data and future projections for quantum computation implemented using trapped ions. While we focus here on architectures targeted for realization using trapped ions, the techniques for quantum computer architecture design, quantum fault-tolerance, and compilation described in this lecture are applicable to many other physical technologies that may be viable candidates for building a large-scale quantum computing system. We also discuss general issues involved with programming a quantum computer as well as a discussion of work on quantum architectures based on quantum teleportation. Finally, we consider some of the open issues remaining in the design of quantum computers.
Elementary Gates for Quantum Computation Tycho Sleator

We show that a set of gates that consists of all one-bit quantum gates (U(2)) and the two-bit exclusive-or gate (that maps Boolean values (x; y) to (x; xy)) is universal in the sense that all unitary operations on arbitrarily many bits n (U(2 n)) can be expressed as compositions of these gates. We investigate the number of the above gates required to implement other gates, such as generalized Deutsch-Toooli gates, that apply a speciic U(2) transformation to one input bit if and only if the logical AND of all remaining input bits is satissed. These gates play a central role in many proposed constructions of quantum computational networks. We derive upper and lower bounds on the exact number of elementary gates required to build up a variety of two-and three-bit quantum gates, the asymptotic number required for n-bit Deutsch-Toooli gates, and make some observations about the number required for arbitrary n-bit unitary operations.
Time series: data analysis and theory [Book Review]

Cox, Matthew L. Miller, and Jeffrey A. Bloom. San Diego,  2002, 576 pp. $69.96 (hardbound). A key ingredient to copyright protection, digital watermarking provides a solution to the illegal copying of material. It also has broader uses in recording and electronic transaction tracking. This book explains “the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied.” [book notes] The authors are extensively experienced in digital watermarking technologies. Cox recently joined the NEC Research Institute after a five-year stint at AT&T Bell Labs. Miller’s interest began at AT&T Bell Labs in 1979. He also is employed at NEC. Bloom is a researcher in digital watermarking at the Sarnoff Corporation. His acquaintance with the field began at Signafy, Inc. and continued through his employment at NEC Research Institute. The book features the following: Review of the underlying principles of watermarking relevant for image, video, and audio; Discussion of a wide variety of applications, theoretical principles, detection and embedding concepts, and key properties; Examination of copyright protection and other applications; Presentation of a series of detailed examples that illustrate watermarking concepts and practices;  Comprehensive glossary of terms. “The authors provide a comprehensive overview of digital watermarking, rife with detailed examples and grounded within strong theoretical framework. Digital Watermarking will serve as a valuable introduction as well as a useful reference for those engaged in the field.”—Walter Bender, Director, M.I.T.
Transforming Models with ATL

This paper presents ATL (ATLAS Transformation Language): a hybrid model transformation language that allows both declarative and imperative constructs to be used in transformation definitions. The paper describes the language syntax and semantics by using examples. ATL is supported by a set of development tools such as an editor, a compiler, a virtual machine, and a debugger. A case study shows the applicability of the language constructs. Alternative ways for implementing the case study are outlined. In addition to the current features, the planned future ATL features are briefly
An Investigation of Factors affecting the Use of ICT for Teaching in the Western Cape Schools

The Khanya project has been equipping schools and educators with ICT skills and equipment to be used in the curriculum delivery in South Africa. However, research and anecdotal evidence show that there is low adoption rate of ICT among educators in Khanya schools. This interpretive study sets out to analyse the factors which are preventing the educators from using the technology in their work. The perspective of limited access and/or use of ICT as deprivation of capabilities provides a conceptual base for this paper. We employed Sen’s Capability Approach as a conceptual lens to examine the educators’ situation regarding ICT for teaching and learning. Data was collected through in-depth interviews with fourteen educators and two Khanya personnel. The results of the study show that there are a number of factors (personal, social and environmental) which are preventing the educators from realising their potential capabilities from the ICT.
Artificial Intelligence – Consumers and Industry Impact

Broadly, artificial intelligence (AI) mainly entails technology constellations such as machine learning, natural language processing, perception, and reasoning since it is difficult to define . Even though the field’s application and principles have undergone investigation for more than sixty-five years, modern improvements, attendant society excitement, and uses ensured its return to focus. The influence of the previous artificial intelligence systems is evident, introducing both opportunities and challenges, which enables the integration of future AI advances into the economic and social environments. It is apparent that most people today view AI as a robotics concept but it essentially incorporates broader technology ranges that are used widely . From search engines to speech recognition, to learning/gaming structures and object detection, AI application has the potential to intensify in the human daily lives. The application is already experiencing use in the world of business as companies seek to study the needs of the consumers, as well as, other fields including healthcare and crime investigation. In this paper, I will discuss the perceptions of consumers regarding artificial intelligence and outline its impact in retail, healthcare, crime investigation, and employment.
Economic Analysis of Ransomware

We present in this work an economic analysis of ransomware, with relevant data from Cryptolocker, CryptoWall, TeslaCrypt and other major strands. We include a detailed study of the impact that different price discrimination strategies can have on the success of a ransomware family, examining uniform pricing, optimal price discrimination and bargaining strategies and analysing their advantages and limitations. In addition, we present results of a preliminary survey that can helps in estimating an optimal ransom value. We discuss at each stage whether the different schemes we analyse have been encountered already in existing malware, and the likelihood of them being implemented and becoming successful. We hope this work will help to gain some useful insights for predicting how ransomware may evolve in the future and be better prepared to counter its current and future threat.
Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.
Constructing an Interactive Natural Language Interface for Relational Databases

Natural language has been the holy grail of query interface designers, but has generally been considered too hard to work with, except in limited specific circumstances. In this paper, we describe the architecture of an interactive natural language query interface for relational databases. Through a carefully limited interaction with the user, we are able to correctly interpret complex natural language queries, in a generic manner across a range of domains. By these means, a logically complex English language sentence is correctly translated into a SQL query, which may include aggregation, nesting, and various types of joins, among other things, and can be evaluated against an RDBMS. We have constructed a system, NaLIR (Natural Language Interface for Relational databases), embodying these ideas. Our experimental assessment, through user studies, demonstrates that NaLIR is good enough to be usable in practice: even naive users are able to specify quite complex ad-hoc queries.