nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Make Better Mistakes: Semantics-aware Visual Food Recognition

We propose a visual food recognition framework that integrates the inherent semantic relationships among fine-grained classes. Our method learns semantics-aware features by formulating a multi-task loss function on top of a convolutional neural network (CNN) architecture. It then refines the CNN predictions using a random walk based smoothing procedure, which further exploits the rich semantic information. We evaluate our algorithm on a large "food-in-the-wild" benchmark, as well as a challenging dataset of restaurant food dishes with very few training images. The proposed method achieves higher classification accuracy than a baseline which directly fine-tunes a deep learning network on the target dataset. Furthermore, we analyze the consistency of the learned model with the inherent semantic relationships among food categories. Results show that the proposed approach provides more semantically meaningful results than the baseline method, even in cases of mispredictions.
Fake News: A Definition

Despite being a new term, ‘fake news’ has evolved rapidly. This paper argues that it should be reserved for cases of deliberate presentation of (typically) false or misleading claims as news, where these are misleading by design. The phrase ‘by design’ here refers to systemic features of the design of the sources and channels by which fake news propagates and, thereby, manipulates the audience’s cognitive processes. This prospective definition is then tested: first, by contrasting fake news with other forms of public disinformation; second, by considering whether it helps pinpoint conditions for the (recent) proliferation of fake news.  En dépit de son utilisation récente, l’expression «fausses nouvelles» a évolué rapidement. Cet article soutient qu'elle devrait être réservée aux présentations intentionnelles d’allégations (typiquement) fausses ou trompeuses comme si elles étaient des nouvelles véridiques et où elles sont faussées à dessein. L'expression «à dessein» fait ici référence à des caractéristiques systémiques de la conception des sources et des canaux par lesquels les fausses nouvelles se propagent et par conséquent, manipulent les processus cognitifs du public. Cette définition prospective est ensuite mise à l’épreuve: d'abord, en opposant les fausses nouvelles à d'autres formes de désinformation publique; deuxièmement, en examinant si elle aide à cerner les conditions de la prolifération (récente) de fausses nou-
Requirements engineering: from craft to discipline

Getting the right software requirements under the right environment assumptions is a critical precondition for developing the right software. This task is intrinsically difficult. We need to produce a complete, adequate, consistent, and well-structured set of measurable requirements and assumptions from incomplete, imprecise, and sparse material originating from multiple, often conflicting sources. The system we need to consider comprises software and environment components including people and devices.
 A rich system model may significantly help us in this task. Such model must integrate the intentional, structural, functional, and behavioral facets of the system being conceived. Rigorous techniques are needed for model construction, analysis, exploitation, and evolution. Such techniques should support early and incremental reasoning about partial models for a variety of purposes including satisfaction arguments, property checks, animations, the evaluation of alternative options, the analysis of risks, threats, and conflicts, and traceability management. The tension between technical precision and practical applicability calls for a suitable mix of heuristic, deductive, and inductive forms of reasoning on a suitable mix of declarative and operational specifications. Formal techniques should be deployed only when and where needed, and kept hidden wherever possible. The paper provides a retrospective account of our research efforts and practical experience along this route. Problem-oriented abstractions, analyzable models, and constructive techniques were permanent concerns.
Big Data for Industry 4.0: A Conceptual Framework

Exponential growth in data volume originating from Internet of Things sources and information services drives the industry to develop new models and distributed tools to handle big data. In order to achieve strategic advantages, effective use of these tools and integrating results to their business processes are critical for enterprises. While there is an abundance of tools available in the market, they are underutilized by organizations due to their complexities. Deployment and usage of big data analysis tools require technical expertise which most of the organizations don't yet possess. Recently, the trend in the IT industry is towards developing prebuilt libraries and dataflow based programming models to abstract users from low-level complexities of these tools. After briefly analyzing trends in the literature and industry, this paper presents a conceptual framework which offers a higher level of abstraction to increase adoption of big data techniques as part of Industry 4.0 vision in future enterprises.
Network information flow

We introduce a new class of problems callednetwork information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be mulitcast to certain sets of destinations. We assume that the information sources are mutually independent. The problem is to characterize the admissible coding rate region. This model subsumes all previously studied models along the same line. In this paper, we study the problem with one information source, and we have obtained a simple characterization of the admissible coding rate region. Our result can be regarded as the Max-flow Min-cut Theorem for network information flow. Contrary to one’s intuition, our work reveals that it is in general not optimal to regard the information to be multicast as a “fluid” which can simply be routed or replicated. Rather, by employing coding at the nodes, which we refer to asnetwork coding, bandwidth can in general be saved. This finding may have significant impact on future design of switching systems.
Cybersecurity Information Sharing: a Framework for Sustainable Information Security Management in UK SME Supply Chains

UK small to medium sized enterprises (SMEs) are suffering increasing levels of cybersecurity breaches and are a major point of vulnerability in the supply chain networks in which they participate. A key factor for achieving optimal security levels within supply chains is the management and sharing of cybersecurity information associated with specific metrics. Such information sharing schemes amongst SMEs in a supply chain network, however, would give rise to a certain level of risk exposure. In response, the purpose of this paper is to assess the implications of adopting select cybersecurity metrics for information sharing in SME supply chain consortia. Thus, a set of commonly used metrics in a prototypical cybersecurity scenario were chosen and tested from a survey of 17 UK SMEs. The results were analysed in respect of two variables; namely, usefulness of implementation and willingness to share across supply chains. Consequently, we propose a Cybersecurity Information Sharing Taxonomy for identifying risk exposure categories for SMEs sharing cybersecurity information, which can be applied to developing Information Sharing Agreements (ISAs) within SME supply chain consortia.
Optimization and stabilization of trajectories for constrained dynamical systems

Contact constraints, such as those between a foot and the ground or a hand and an object, are inherent in many robotic tasks. These constraints define a manifold of feasible states; while well understood mathematically, they pose numerical challenges to many algorithms for planning and controlling whole-body dynamic motions. In this paper, we present an approach to the synthesis and stabilization of complex trajectories for both fully-actuated and underactuated robots subject to contact constraints. We introduce a trajectory optimization algorithm (DIRCON) that extends the direct collocation method, naturally incorporating manifold constraints to produce a nominal trajectory with third-order integration accuracy-a critical feature for achieving reliable tracking control. We adapt the classical time-varying linear quadratic regulator to produce a local cost-to-go in the manifold tangent plane. Finally, we descend the cost-to-go using a quadratic program that incorporates unilateral friction and torque constraints. This approach is demonstrated on three complex walking and climbing locomotion examples in simulation.
Impact of CRM Factors on Customer Satisfaction and Loyalty

This paper examined the impact of customer relationship management (CRM) elements on customer satisfaction and loyalty. CRM is one of the critical strategies that can be employed by organizations to improve competitive advantage. Four critical CRM elements are measured in this study are behavior of the employees, quality of customer services, relationship development and interaction management. The study was performed at a departmental store in Tehran, Iran. The study employed quantitative approach and base on 300 respondents. Multiple regression analysis is used to examine the relationship of the variables. The finding shows that behavior of the employees is significantly relate and contribute to customer satisfaction and loyalty.
Don't Fear the Reaper: Refuting Bostrom's Superintelligence Argument

In recent years prominent intellectuals have raised ethical concerns about the consequences of artificial intelligence. One concern is that an autonomous agent might modify itself to become " superintelligent " and, in supremely effective pursuit of poorly specified goals, destroy all of humanity. This paper considers and rejects the possibility of this outcome. We argue that this scenario depends on an agent's ability to rapidly improve its ability to predict its environment through self-modification. Using a Bayesian model of a reasoning agent, we show that there are important limitations to how an agent may improve its predictive ability through self-modification alone. We conclude that concern about this artificial intelligence outcome is misplaced and better directed at policy questions around data access and storage. The appetite of the public and prominent intellectuals for the study of the ethical implications of artificial intelligence has increased in recent years. One captivating possibility is that artificial intelligence research might result in a 'superintelli-gence' that puts humanity at risk.  In focuses on the conditions of an " intelligence explosion " that would lead to a dominant machine intelligence averse to humanity. Section 2 argues that rather than speculating broadly about general artificial intelligence, we can predict outcomes of artificial intelligence by considering more narrowly a few tasks that are essential to instrumental reasoning. Section 3 considers recalcitrance, the resistance of a system to improvements to its own intelligence, and the ways it can limit intelligence explosion. Section 4 contains …
Smarnet: Teaching Machines to Read and Comprehend Like Human

Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question. Many existing approaches on MC task are suffering the inefficiency in some bottlenecks, such as insufficient lexical understanding, complex question-passage interaction, incorrect answer extraction and so on. In this paper, we address these problems from the viewpoint of how humans deal with reading tests in a scientific way. Specifically, we first propose a novel lexical gating mechanism to dynamically combine the words and characters representations. We then guide the machines to read in an interactive way with attention mechanism and memory network. Finally we add a checking layer to refine the answer for insurance. The extensive experiments on two popular datasets SQuAD and TriviaQA show that our method exceeds considerable performance than most stateof-the-art solutions at the time of submission.
An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews

Irony is an important device in human communication, both in everyday spoken conversations as well as in written texts including books, websites, chats, reviews, and Twitter messages among others. Specific cases of irony and sarcasm have been studied in different contexts but, to the best of our knowledge, only recently the first publicly available corpus including annotations about whether a text is ironic or not has been published by Filatova (2012). However, no baseline for classification of ironic or sarcastic reviews has been provided. With this paper, we aim at closing this gap. We formulate the problem as a supervised classification task and evaluate different classifiers, reaching an F1-measure of up to 74 % using logistic regression. We analyze the impact of a number of features which have been proposed in previous research as well as combinations of them.
Unsupervised Learning of Distributions of Binary Vectors Using 2-Layer Networks

David Haussler Computer and Information Sciences University of California Santa Cruz Santa Cruz , CA 95064 We study a particular type of Boltzmann machine with a bipartite graph structure called a harmonium. Our interest is in using such a machine to model a probability distribution on binary input vectors . We analyze the class of probability distributions that can be modeled by such machines. showing that for each n ~ 1 this class includes arbitrarily good appwximations to any distribution on the set of all n-vectors of binary inputs. We then present two learning algorithms for these machines .. The first learning algorithm is the standard gradient ascent heuristic for computing maximum likelihood estimates for the parameters (i.e. weights and thresholds) of the modeL Here we give a closed form for this gradient that is significantly easier to compute than the corresponding gradient for the general Boltzmann machine . The second learning algorithm is a greedy method that creates the hidden units and computes their weights one at a time. This method is a variant of the standard method for projection pursuit density estimation . We give experimental results for these learning methods on synthetic data and natural data from the domain of handwritten digits.
Natural language processing: a prolog perspective

Natural language processing (NLP) is a vibrant field of interdisciplinary Computer Science research. Ultimately, NLP seeks to build intelligence into software so that software will be able to process a natural language as skillfully and artfully as humans. Prolog, a general purpose logic programming language, has been used extensively to develop NLP applications or components thereof. This report is concerned with introducing the interested reader to the broad field of NLP with respect to NLP applications that are built in Prolog or from Prolog components.
An efficient design and implementation of LSM-tree based key-value store on open-channel SSD

Various key-value (KV) stores are widely employed for data management to support Internet services as they offer higher efficiency, scalability, and availability than relational database systems. The log-structured merge tree (LSM-tree) based KV stores have attracted growing attention because they can eliminate random writes and maintain acceptable read performance. Recently, as the price per unit capacity of NAND flash decreases, solid state disks (SSDs) have been extensively adopted in enterprise-scale data centers to provide high I/O bandwidth and low access latency. However, it is inefficient to naively combine LSM-tree-based KV stores with SSDs, as the high parallelism enabled within the SSD cannot be fully exploited. Current LSM-tree-based KV stores are designed without assuming SSD's multi-channel architecture.
 To address this inadequacy, we propose LOCS, a system equipped with a customized SSD design, which exposes its internal flash channels to applications, to work with the LSM-tree-based KV store, specifically LevelDB in this work. We extend LevelDB to explicitly leverage the multiple channels of an SSD to exploit its abundant parallelism. In addition, we optimize scheduling and dispatching polices for concurrent I/O requests to further improve the efficiency of data access. Compared with the scenario where a stock LevelDB runs on a conventional SSD, the throughput of storage system can be improved by more than 4X after applying all proposed optimization techniques.
Metacognitive control and strategy selection: deciding to practice retrieval during learning.

Retrieval practice is a potent technique for enhancing learning, but how often do students practice retrieval when they regulate their own learning? In 4 experiments the subjects learned foreign-language items across multiple study and test periods. When items were assigned to be repeatedly tested, repeatedly studied, or removed after they were recalled, repeated retrieval produced powerful effects on learning and retention. However, when subjects were given control over their own learning and could choose to test, study, or remove items, many subjects chose to remove items rather than practice retrieval, leading to poor retention. In addition, when tests were inserted in the learning phase, attempting retrieval improved learning by enhancing subsequent encoding during study. But when students were given control over their learning they did not attempt retrieval as early or as often as they should to promote the best learning. The experiments identify a compelling metacognitive illusion that occurs during self-regulated learning: Once students can recall an item they tend to believe they have "learned" it. This leads students to terminate practice rather than practice retrieval, a strategy choice that ultimately results in poor retention.
Separate visual pathways for perception and action

Accumulating neuropsychological, electrophysiological and behavioural evidence suggests that the neural substrates of visual perception may be quite distinct from those underlying the visual control of actions. In other words, the set of object descriptions that permit identification and recognition may be computed independently of the set of descriptions that allow an observer to shape the hand appropriately to pick up an object. We propose that the ventral stream of projections from the striate cortex to the inferotemporal cortex plays the major role in the perceptual identification of objects, while the dorsal stream projecting from the striate cortex to the posterior parietal region mediates the required sensorimotor transformations for visually guided actions directed at such objects.
Connotation in Translation

We present a pilot study analyzing the connotative language found in a bilingual corpus of French and English headlines. We find that (1) manual annotation of connotation at the word-level is more reliable than using segment-level judgments, (2) connotation polarity is often, but not always, preserved in reference translations produced by humans, (3) machine translated text does not preserve the connotative language identified by an English connotation lexicon. These lessons will helps us build new resources to learn better models of connotation and translation.
Size and Value Anomalies under Regime Shifts ∗

This paper finds strong evidence of time-variations in the joint distribution of returns on a stock market portfolio and portfolios tracking sizeand value effects. Mean returns, volatilities and correlations between these equity portfolios are found to be driven by underlying regimes that introduce short-run market timing opportunities for investors. The magnitude of the premia on the size and value portfolios and their hedging properties are found to vary significantly across regimes. Regimes are also found to have a large impact on the optimal asset allocation especially under rebalancing and on investors’ welfare.
International Diversification from a UK Perspective Exploring the Risk-Reduction Effects of Diversification for UK Investors April 2009

The market turmoil of 2008 highlighted the importance of risk management to investors in the UK and worldwide. Realized risk levels and risk forecasts from the Barra Europe Equity Model (EUE2L) are both currently at the highest level for the last two decades. According to portfolio theory, institutional investors can gain significant risk-reduction and return-enhancement benefits from venturing out of their domestic markets. These effects from international diversification are due to imperfect correlations among markets. In this paper, we explore the historical diversification effects of an international allocation for UK investors. We illustrate that investing only in the UK market can be considered an active deviation from a global benchmark. Although a domestic allocation to UK large-cap stocks has significant international exposure when revenue sources are taken into account, as an active deviation from a global benchmark a UK domestic strategy has high concentration, leading to high asset-specific risk, and significant style and industry tilts. We show that an international allocation resulted in higher returns and lower risk for a UK investor in the last one, three, five, and ten years. In GBP terms, the MSCI All Country World Investable Market Index (ACWI IMI) — a global index that could be viewed as a proxy for a global portfolio — achieved higher return and lower risk compared to the MSCI UK Index during these periods. A developed market minimum-variance portfolio, represented by the MSCI World Minimum Volatility Index, 1 The market turmoil of 2008 highlighted the importance of risk management to investors in the UK and worldwide.  1 illustrates that the historical standard deviation of the MSCI UK Index is now near the highest level in recent history. The risk forecast for the index, obtained using the Barra Europe Equity Model, typically showed still better risk and return performance during these periods. The decreases in risk represented by allocations to MSCI ACWI IMI and the MSCI World Minimum Volatility Index were robust based on four different measures of portfolio risk. We also consider a stepwise approach to international diversification, sequentially adding small cap and international assets to a large cap UK portfolio. We show that this approach also reduced risk during the observed period, but we did not find evidence that it was more efficient for risk reduction than a passive allocation to MSCI ACWI IMI.
GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction

Medians calculated from participating systems in each task (6 unsupervised TS & ES, 5 unsupervised TA & EA, 4 unsupervised CR, 11 supervised TS & ES, 10 supervised TA & EA (modality), 9 supervised EA (others), 8 supervised CR. • Baselines: MEMORIZE for TS, ES, TA, EA. CLOSEST for CR. • TA & EA – One CRF model per attribute – Same features as for TS and ES – Extended window of ±3 • CR – Supervised classification: Gradient boosted trees – Classify each candidate relation based on type – Candidate relations are all intra-sentence entity pairs – Features: • Entity features: type, attributes, text, suffix, semantic role labeling •  token distance, constituency and dependency tree paths (n-grams)