nils-herrmann/clean_scirepeval huggingface dataset:
AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context

Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS messages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security-sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security-sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.
Spatial memory deficits in patients with lesions to the right hippocampus and to the right parahippocampal cortex

Spatial memory tasks, performance of which is known to be sensitive to hippocampal lesions in the rat, or to medial temporal lesions in the human, were administered in order to investigate the effects of selective damage to medial temporal lobe structures of the human brain. The patients had undergone thermo-coagulation with a single electrode along the amygdalo-hippocampal axis in an attempt to alleviate their epilepsy. With this surgical technique, lesions to single medial temporal lobe structures can be carried out. The locations of the lesions were assessed by means of digital high-resolution magnetic resonance imaging and software allowing a 3-D reconstruction of the brain. A break in the collateral sulcus, dividing it into the anterior collateral sulcus and the posterior collateral sulcus is reported. This division may correspond to the end of the entorhinal/perirhinal cortex and the start of the parahippocampal cortex. The results confirmed the role of the right hippocampus in visuo-spatial memory tasks (object location, Rey-Osterrieth Figure with and without delay) and the left for verbal memory tasks (Rey Auditory Verbal Learning Task with delay). However, patients with lesions either to the right or to the left hippocampus were unimpaired on several memory tasks, including a spatial one, with a 30 min delay, designed to be analogous to the Morris water maze. Patients with lesions to the right parahippocampal cortex were impaired on this task with a 30 min delay, suggesting that the parahippocampal cortex itself may play an important role in spatial memory.
Demonstrating AI-enabled SQLQueries over Relational Data using a Cognitive Database

This paper demonstrates key capabilities of Cognitive Database, a novel AI-enabled relational database system which uses an unsupervised neural network model to facilitate semantic queries over relational data. The neural network model, called word embedding, operates on an unstructured view of the database and builds a vector model that captures latent semantic context of database entities of different types. The vector model is then seamlessly integrated into the SQL infrastructure and exposed to the users via a new class of SQL-based analytics queries known as cognitive intelligence (CI) queries. The cognitive capabilities enable complex queries over multi-modal data such as semantic matching, inductive reasoning queries such as analogies, and predictive queries using entities not present in a database. We plan to demonstrate the end-to-end execution flow of the cognitive database using a Spark based prototype. Furthermore, we demonstrate the use of CI queries using a publicaly available enterprise financial dataset (with text and numeric values). A Jupyter Notebook python based implementation will also be presented.
Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks.

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.
Using Content-Based Filtering for Recommendation

Finding information on a large web site can be a difficult and time-consuming process. Recommender systems can help users find information by providing them with personalized suggestions. In this paper the recommender system PRES is described that uses content-based filtering techniques to suggest small articles about home improvements. A domain such as this implicates that the user model has to be very dynamic and learned from positive feedback only. The relevance feedback method seems to be a good candidate for learning such a user model, as it is both efficient and dynamic.
One-Pot Algal Biodiesel Production in Supercritical Carbon Dioxide

To advance the realization of algae as a feedstock for biodiesel, process technologies and closed-loop biomass use must be optimized. Life-cycle analysis (LCA) of the biodiesel production process highlights the potential significant impact of improvements in the extraction of algal lipids and conversion into biodiesel. Further, a single-step lipid extraction and transesterification process was shown to have the highest energy rewards. This work investigates the potential for using scCO2 for the extraction and conversion process. scCO2 is shown to be an effective and selective solvent for extracting triglycerides from algal biomass. This work also explores the fundamental science necessary to achieve a one-pot approach that both extracts and transesterifies lipid from algae using supercritical carbon dioxide/methanol (scCO2/MeOH) and heterogeneous catalysts. A variety of basic and acidic heterogeneous catalysts have been surveyed for their effectiveness at transesterification of triglyceride (TG) to fatty acid methyl esters (FAME). Further the enhanced solubility of FAME over reaction intermediates, TG, and glycerol, is likely to provide a driving force for reaction. This research offers the foundations for a simple one-pot system wherein biodiesel can be directly, selectively, and sustainably produced from algae for further application in an algae
Effective Approaches to Attention-based Neural Machine Translation

An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. We demonstrate the effectiveness of both approaches on the WMT translation tasks between English and German in both directions. With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems that already incorporate known techniques such as dropout. Our ensemble model using different attention architectures yields a new state-of-the-art result in the WMT’15 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks

Auto-encoders compress input data into a latent-space representation and reconstruct the original data from the representation. This latent representation is not easily interpreted by humans. In this paper, we propose training an auto-encoder that encodes input text into human-readable sentences, and unpaired abstractive summarization is thereby achieved. The auto-encoder is composed of a generator and a reconstructor. The generator encodes the input text into a shorter word sequence, and the reconstructor recovers the generator input from the generator output. To make the generator output human-readable, a discriminator restricts the output of the generator to resemble human-written sentences. By taking the generator output as the summary of the input text, abstractive summarization is achieved without document-summary pairs as training data. Promising results are shown on both English and Chinese corpora.
Apoptosis in the nervous system

Neuronal apoptosis sculpts the developing brain and has a potentially important role in neurodegenerative diseases. The principal molecular components of the apoptosis programme in neurons include Apaf-1 (apoptotic protease-activating factor 1) and proteins of the Bcl-2 and caspase families. Neurotrophins regulate neuronal apoptosis through the action of critical protein kinase cascades, such as the phosphoinositide 3-kinase/Akt and mitogen-activated protein kinase pathways. Similar cell-death-signalling pathways might be activated in neurodegenerative diseases by abnormal protein structures, such as amyloid fibrils in Alzheimer's disease. Elucidation of the cell death machinery in neurons promises to provide multiple points of therapeutic intervention in neurodegenerative diseases.
Ex Machina: Personal Attacks Seen at Scale

The damage personal attacks make to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combines crowdsourcing and machine learning to analyze personal attacks at scale. We show an evaluation method for a classifier in terms of the aggregated number of crowd-workers it can approximate. We apply our methodology to English Wikipedia, generating a corpus of over 100k high quality human-labeled comments and 63M machine-labeled ones from a classifier that is as good as the aggregate of 3 crowd-workers. Using the corpus of machine-labeled scores, our methodology allows us to explore some of the open questions about the nature of online personal attacks. This reveals that the majority of personal attacks on Wikipedia are not the result of a few malicious users, nor primarily the consequence of allowing anonymous contributions.
A survey of secure middleware for the Internet of Things

The rapid growth of small Internet connected devices, known as the Internet of Things (IoT), is creating a new set of challenges to create secure, private infrastructures. This paper reviews the current literature on the challenges and approaches to security and privacy in the Internet of Things, with a strong focus on how these aspects are handled in IoT middleware. We focus on IoT middleware because many systems are built from existing middleware and these inherit the underlying security properties of the middleware framework. The paper is composed of three main sections. Firstly, we propose a matrix of security and privacy threats for IoT. This matrix is used as the basis of a widespread literature review aimed at identifying requirements on IoT platforms and middleware. Secondly, we present a structured literature review of the available middleware and how security is handled in these middleware approaches. We utilise the requirements from the first phase to evaluate. Finally, we draw a set of conclusions and identify further work in this area. Subjects Computer Networks and Communications, Embedded Computing, Real-Time and Embedded Systems, Security and Privacy, World Wide Web and Web
Effects of treatment in women with gestational diabetes mellitus: systematic review and meta-analysis

To summarise the benefits and harms of treatments for women with gestational diabetes mellitus.



Systematic review and meta-analysis of randomised controlled trials.



Embase, Medline, AMED, BIOSIS, CCMed, CDMS, CDSR, CENTRAL, CINAHL, DARE, HTA, NHS EED, Heclinet, SciSearch, several publishers' databases, and reference lists of relevant secondary literature up to October 2009.  Included studies were randomised controlled trials of specific treatment for gestational diabetes compared with usual care or "intensified" compared with "less intensified" specific treatment.



Five randomised controlled trials matched the inclusion criteria for specific versus usual treatment. All studies used a two step approach with a 50 g glucose challenge test or screening for risk factors, or both, and a subsequent 75 g or 100 g oral glucose tolerance test. Meta-analyses did not show significant differences for most single end points judged to be of direct clinical importance. In women specifically treated for gestational diabetes, shoulder dystocia was significantly less common (odds ratio 0.40, 95% confidence interval 0.21 to 0.75), and one randomised controlled trial reported a significant reduction of pre-eclampsia (2.5 v 5.5%, P=0.02). For the surrogate end point of large for gestational age infants, the odds ratio was 0.48 (0.38 to 0.62). In the 13 randomised controlled trials of different intensities of specific treatments, meta-analysis showed a significant reduction of shoulder dystocia in women with more intensive treatment (0.31, 0.14 to 0.70).



Treatment for gestational diabetes, consisting of treatment to lower blood glucose concentration alone or with special obstetric care, seems to lower the risk for some perinatal complications. Decisions regarding treatment should take into account that the evidence of benefit is derived from trials for which women were selected with a two step strategy (glucose challenge test/screening for risk factors and oral glucose tolerance test).
Predictive Representations of State

We show that states of a dynamical system can be usefully represented by multi-step, action-conditional predictions of future observations. State representations that are grounded in data in this way may be easier to learn, generalize better, and be less dependent on accurate prior models than, for example, POMDP state representations. Building on prior work by Jaeger and by Rivest and Schapire, in this paper we compare and contrast a linear specialization of the predictive approach with the state representations used in POMDPs and in k-order Markov models. Ours is the first specific formulation of the predictive idea that includes both stochasticity and actions (controls). We show that any system has a linear predictive state representation with number of predictions no greater than the number of states in its minimal POMDP model. In predicting or controlling a sequence of observations, the concepts of state and state estimation inevitably arise. There have been two dominant approaches. The generative-model approach, typified by research on partially observable Markov decision processes (POMDPs), hypothesizes a structure for generating observations and estimates its state and state dynamics. The history-based approach, typified by k-order Markov methods, uses simple functions of past observations as state, that is, as the immediate basis for prediction and control. (The data flow in these two approaches are diagrammed in Figure 1.) Of the two, the generative-model approach is more general. The model's internal state gives it temporally unlimited memorythe ability to remember an event that happened arbitrarily long ago--whereas a history-based approach can only remember as far back as its history extends. The bane of generative-model approaches is that they are often strongly dependent on a good model of the system's dynamics. Most uses of POMDPs, for example, assume a perfect dynamics model and attempt only to estimate state. There are algorithms for simultaneously estimating state and dynamics (e.g., Chrisman, 1992), analogous to the Baum-Welch algorithm for the uncontrolled case (Baum et al., 1970), but these are only effective at tuning parameters that are already approximately correct (e.g., Shatkay & Kaelbling, 1997). observations (and actions) (a) state 1-----1-----1..rep'n observations¢E (and actions) / state t/' rep'n 1-step --+ .
Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems

Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multiantenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time-division multiple access protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamforming at the AP, the central processing unit frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP’s total energy consumption subject to the users’ individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semiclosed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.
The World-Wide Web: Quagmire or Gold Mine?

Skeptics believe the Web is too unstructured for Web mining to succeed. Indeed, data mining has been applied traditionally to databases, yet much of the information on the Web lies buried in documents designed for human consumption such as home pages or product catalogs. Furthermore, much of the information on the Web is presented in natural-language text with no machine-readable semantics; HTML annotations structure the display of Web pages, but provide little insight into their content. Some have advocated transforming the Web into a massive layered database to facilitate data mining , but the Web is too dynamic and chaotic to be tamed in this manner. Others have attempted to hand code site-specific “wrappers” that facilitate the extraction of information from individual Web resources (e.g., ). Hand coding is convenient but cannot keep up with the explosive growth of the Web. As an alternative, this article argues for the structured Web hypothesis: Information on the Web is sufficiently structured to facilitate effective Web mining. Examples of Web structure include linguistic and typographic conventions, HTML annotations (e.g., <title>), classes of semi-structured documents (e.g., product catalogs), Web indices and directories, and much more. To support the structured Web hypothesis, this article will survey preliminary Web mining successes and suggest directions for future work. Web mining may be organized into the following subtasks:
Learning dashboards: an overview and future research opportunities

In this paper, we present work on learning analytics that aims to support learners and teachers through dashboard applications, ranging from small mobile applications to learnscapes on large public displays. Dashboards typically capture and visualize traces of learning activities, in order to promote awareness, reflection, and sense-making, and to enable learners to define goals and track progress toward these goals. Based on an analysis of our own work and a broad range of similar learning dashboards, we identify HCI issues for this exciting research area.
Transfer learning for image classification with incomplete multiple sources

Transfer learning plays a powerful role in mitigating the discrepancy between test data (target) and auxiliary data (source). There is often the case that multiple sources are available in transfer learning. However, naively combining multiple sources does not lead to valid results, since they will introduce negative transfer as well. Furthermore, each single source from multiple sources may not cover all the labels of the target data. In this paper, we consider the problem that how to better utilize multiple incomplete sources for effective knowledge transfer. To this end, we propose a Bi-directional Low-Rank Transfer learning framework (BLRT). First, we adapt the conventional low-rank transfer learning to multiple sources knowledge transfer scenario. Second, an iterative structure learning is proposed to better use prior knowledge for transfer learning coefficients matrix. Third, a cross-source regularizer is added to couple the same labels from multiple incomplete sources, so that they could jointly compensate missing data from other sources. Experimental results on three groups of databases including face and object images have demonstrated that our method can successfully inherit knowledge from incomplete multiple sources and adapt to the target data successfully.
The relationship between system usage and user satisfaction: a meta-analysis

System usage and user satisfaction are widely accepted and used as surrogate measures of IS success. Past studies attempted to explore the relationship between system usage and user satisfaction but findings are mixed, inconclusive and misleading. The main objective of this research is to better understand and explain the nature and strength of the relationship between system usage and user satisfaction by resolving the existing inconsistencies in the IS research and to validate this relationship empirically as defined in Delone and McLean’s IS success model.  “Meta-analysis” as a research approach was adopted because of its suitability regarding the nature of the research and its capability of dealing with exploring relationships that may be obscured in other approaches to synthesize research findings. Meta-analysis findings contributed towards better explaining the relationship between system usage and user satisfaction, the main objectives of this research.  This research examines critically the past findings and resolves the existing inconsistencies. The meta-analysis findings explain that there exists a significant positive relationship between “system usage” and “user satisfaction” (i.e. r 1⁄4 0:2555) although not very strong. This research empirically validates this relationship that has already been proposed by Delone and McLean in their IS success model. Provides a guide for future research to explore the mediating variables that might affect the relationship between system usage and user satisfaction.  This research better explains the relationship between system usage and user satisfaction by resolving contradictory findings in the past research and contributes to the existing body of knowledge relating to IS success.