nils-herrmann/clean_scirepeval huggingface dataset:
Emergent Translation in Multi-Agent Communication

While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication game where two agents, native speakers of their own respective languages, jointly learn to solve a visual referential task. We find that the ability to understand and translate a foreign language emerges as a means to achieve shared goals. The emergent translation is interactive and multimodal, and crucially does not require parallel corpora, but only monolingual, independent text and corresponding images. Our proposed translation model achieves this by grounding the source and target languages into a shared visual modality, and outperforms several baselines on both word-level and sentence-level translation tasks. Furthermore, we show that agents in a multilingual community learn to translate better and faster than in a bilingual communication setting.
Aspect Based Sentiment Analysis of Movie Reviews: Finding the Polarity Directing Aspects

Sentiment analysis of a movie review plays an important role in understanding the sentiment conveyed by the user towards the movie. In the current work we focus on aspect based sentiment analysis of movie reviews in order to find out the aspect specific driving factors. These factors are the score given to various movie aspects and generally aspects with high driving factors direct the polarity of the review the most. The experiment showed that by giving high driving factors to Movie, Acting and Plot aspects of a movie, we obtained the highest accuracy in the analysis of movie reviews.
Designing for interaction immediacy to enhance social skills of children with autism

Children with Autism Spectrum Disorder often require therapeutic interventions to support engagement in effective social interactions. In this paper, we present the results of a study conducted in three public schools that use an educational and behavioral intervention for the instruction of social skills in changing situational contexts. The results of this study led to the concept of interaction immediacy to help children maintain appropriate spatial boundaries, reply to conversation initiators, disengage appropriately at the end of an interaction, and identify potential communication partners. We describe design principles for Ubicomp technologies to support interaction immediacy and present an example design. The contribution of this work is twofold. First, we present an understanding of social skills in mobile and dynamic contexts. Second, we introduce the concept of interaction immediacy and show its effectiveness as a guiding principle for the design of Ubicomp applications.
Critical Success Factors of Enterprise Resource Planning Systems Implementation Success in China

ERP implementation issues have been given much attention since two decades ago due to its low implementation success. Nearly 90 percent of ERP implementations are late or over budget [16] and the success rate with ERP implementation is about 33%. In China, the success rate of implementing ERP systems is extremely low at 10% [28] which is much lower than that in West countries. This study attempts to study critical success factors affecting enterprise resource planning (ERP) systems implementation success in China with focus on both generic and unique factors. User satisfaction and White’s ABCD classification method are used to judge whether an ERP system implementation is a success or a failure. Survey methodology and structural equation modeling technique of PLS-Graph are used to collect and analyze data. Discussions on the results of data analysis are made.
Malware classification using self organising feature maps and machine activity data

In this article we use machine activity metrics to automatically distinguish between malicious and trusted portable executable software samples. The motivation stems from the growth of cyber attacks using techniques that have been employed to surreptitiously deploy Advanced Persistent Threats (APTs). APTs are becoming more sophisticated and able to obfuscate much of their identifiable features through encryption, custom code bases and inmemory execution. Our hypothesis is that we can produce a high degree of accuracy in distinguishing malicious from trusted samples using Machine Learning with features derived from the inescapable footprint left behind on a computer system during execution. This includes CPU, RAM, Swap use and network traffic at a count level of bytes and packets. These features are continuous and allow us to be more flexible with the classification of samples than discrete features such as API calls (which can also be obfuscated) that form the main feature of the extant literature. We use these continuous data and develop a novel classification method using Self Organizing Feature Maps to reduce over fitting during training through the ability to create unsupervised clusters of similar “behaviour” that are subsequently used as features for classification, rather than using the raw data. We compare our method to a set of machine classification methods that have been applied in previous research and demonstrate an increase of between 7.24% and 25.68% in classification accuracy using our method and an unseen dataset over the range of other machine classification methods that have been applied in previous research.
A new PM machine topology for low-speed, high-torque drives

The paper describes a completely new topology for a low-speed, high-torque permanent brushless magnet machine. Despite being naturally air-cooled, it has a significantly higher torque density than a liquid-cooled transverse-flux machine, whilst its power factor is similar to that of a conventional permanent magnet brushless machine. The high torque capability and low loss density are achieved by combining the actions of a speed reducing magnetic gear and a high speed PM brushless machine within a highly integrated magnetic circuit. In this way, the magnetic limit of the machine is reached before its thermal limit. The principle of operation of such a dasiapseudopsila direct-drive machine is described, and measured results from a prototype machine are presented.
A low-cost high gain substrate integrated waveguide fed patch antenna array for 60-GHz applications

A high gain substrate integrated waveguide (SIW) fed cavity-backed patch antenna array is demonstrated at V-band. The multilayered structure of the proposed array can be fabricated by conventional low-cost single layered printed circuit board (PCB) technology and then realized by stacking and fixing all the layers together. A gain up to 29.7 dBi with a 3-dB gain bandwidth of 16.1%, an impedance bandwidth of 15.3% from 57.5 to 67 GHz for SWR < 2, symmetrically broadside radiation patterns with −40 dB cross-polarizations are achieved.
Semantic Model Vectors for Complex Video Event Recognition

We propose semantic model vectors, an intermediate level semantic representation, as a basis for modeling and detecting complex events in unconstrained real-world videos, such as those from YouTube. The semantic model vectors are extracted using a set of discriminative semantic classifiers, each being an ensemble of SVM models trained from thousands of labeled web images, for a total of 280 generic concepts. Our study reveals that the proposed semantic model vectors representation outperforms-and is complementary to-other low-level visual descriptors for video event modeling. We hence present an end-to-end video event detection system, which combines semantic model vectors with other static or dynamic visual descriptors, extracted at the frame, segment, or full clip level. We perform a comprehensive empirical study on the 2010 TRECVID Multimedia Event Detection task (http://www.nist.gov/itl/iad/mig/med10.cfm), which validates the semantic model vectors representation not only as the best individual descriptor, outperforming state-of-the-art global and local static features as well as spatio-temporal HOG and HOF descriptors, but also as the most compact. We also study early and late feature fusion across the various approaches, leading to a 15% performance boost and an overall system performance of 0.46 mean average precision. In order to promote further research in this direction, we made our semantic model vectors for the TRECVID MED 2010 set publicly available for the community to use (http://www1.cs.columbia.edu/~mmerler/SMV.html).
Experimental Results for Dexterous Quadruped Locomotion Planning with RoboSimian

RoboSimian is a quadruped robot inspired by an ape-like morphology, with four symmetric limbs that provide a large dexterous workspace and high torque output capabilities. Advantages of using RoboSimian for rough terrain locomotion include (1) its large, stable base of support, and (2) existence of redundant kinematic solutions, toward avoiding collisions with complex terrain obstacles. However, these same advantages provide significant challenges in experimental implementation of walking gaits. Specifically: (1) a wide support base results in high variability of required body pose and foothold heights, in particular when compared with planning for humanoid robots, (2) the long limbs on RoboSimian have a strong proclivity for self-collision and terrain collision, requiring particular care in trajectory planning, and (3) having rear limbs outside the field of view requires adequate perception with respect to a world map. In our results, we present a tractable means of planning statically stable and collision-free gaits, which combines practical heuristics for kinematics with traditional randomized (RRT) search algorithms. In planning experiments, our method outperforms other tested methodologies. Finally, real-world testing indicates that perception limitations provide the greatest challenge in real-world implementation.
Behavior-Rule Based Intrusion Detection Systems for Safety Critical Smart Grid Applications

In this paper, a behavior-rule based intrusion detection system (BRIDS) is proposed for securing head-ends (HEs), distribution access points/data aggregation points (DAPs) and subscriber energy meters (SEMs) of a modern electrical grid in which continuity of operation is of the utmost importance. The impact of attacker behaviors on the effectiveness of a behavior-rule intrusion detection design is investigated. Using HEs, DAPs and SEMs as examples, it is demonstrated that a behavior-rule based intrusion detection technique can effectively trade false positives for a high detection probability to cope with sophisticated and hidden attackers to support ultra safe and secure applications. It is shown that BRIDS outperforms contemporary anomaly-based IDSs via comparative analysis.
Foldable interactive displays

Modern computer displays tend to be in fixed size, rigid, and rectilinear rendering them insensitive to the visual area demands of an application or the desires of the user. Foldable displays offer the ability to reshape and resize the interactive surface at our convenience and even permit us to carry a very large display surface in a small volume. In this paper, we implement four interactive foldable display designs using image projection with low-cost tracking and explore display behaviors using orientation sensitivity.
Indoor 5G 3GPP-like channel models for office and shopping mall environments

Future mobile communications systems are likely to be very different to those of today with new service innovations driven by increasing data traffic demand, increasing processing power of smart devices and new innovative applications. To meet these service demands the telecommunications industry is converging on a common set of 5G requirements which includes network speeds as high as 10 Gbps, cell edge rate greater than 100 Mbps, and latency of less than 1 msec. To reach these 5G requirements the industry is looking at new spectrum bands in the range up to 100 GHz where there is spectrum availability for wide bandwidth channels. For the development of new 5G systems to operate in bands up to 100 GHz there is a need for accurate radio propagation models which are not addressed by existing channel models developed for bands below 6 GHz. This paper presents a preliminary overview of the 5G channel models for bands up to 100 GHz in indoor offices and shopping malls, derived from extensive measurements across a multitude of bands. These studies have found some extensibility of the existing 3GPP models (e.g. 3GPP TR36.873) to the higher frequency bands up to 100 GHz. The measurements indicate that the smaller wavelengths introduce an increased sensitivity of the propagation models to the scale of the environment and show some frequency dependence of the path loss as well as increased occurrence of blockage. Further, the penetration loss is highly dependent on the material and tends to increase with frequency. The small-scale characteristics of the channel such as delay spread and angular spread and the multipath richness is somewhat similar over the frequency range, which is encouraging for extending the existing 3GPP models to the wider frequency range. Further work will be carried out to complete these models, but this paper presents the first steps for an initial basis for the model development.
Positional language models for information retrieval

Although many variants of language models have been proposed for information retrieval, there are two related retrieval heuristics remaining "external" to the language modeling approach: (1) proximity heuristic which rewards a document where the matched query terms occur close to each other; (2) passage retrieval which scores a document mainly based on the best matching passage. Existing studies have only attempted to use a standard language model as a "black box" to implement these heuristics, making it hard to optimize the combination parameters.
 In this paper, we propose a novel positional language model (PLM) which implements both heuristics in a unified language model. The key idea is to define a language model for each position of a document, and score a document based on the scores of its PLMs. The PLM is estimated based on propagated counts of words within a document through a proximity-based density function, which both captures proximity heuristics and achieves an effect of "soft" passage retrieval. We propose and study several representative density functions and several different PLM-based document ranking strategies. Experiment results on standard TREC test collections show that the PLM is effective for passage retrieval and performs better than a state-of-the-art proximity-based retrieval model.
F-Logic: A Higher-Order language for Reasoning about Objects, Inheritance, and Scheme

We propose a database logic which accounts in a clean declarative fashion for most of the “object-oriented” features such as object identity, complex objects, inheritance, methods, etc. Furthermore, database schema is part of the object language, which allows the user to browse schema and data using the same declarative formalism. The proposed logic has a formal semantics and a sound and complete resolution-based proof procedure, which makes it also computationally attractive.
DC-Link Stabilization and Voltage Sag Ride-Through of Inverter Drives

Previous results concerning instability of the dc link in inverter drives fed from a dc grid or via a rectifier are extended. It is shown that rectifier-inverter drives equipped with small (film) dc-link capacitors may need active stabilization. The impact of limited bandwidth and switching frequency in the inverter-motor current control loop is considered, and recommendations for selection of the dc-link capacitor, the switching frequency, and the dc-link stabilization control law in relation to each other are given. This control law is incorporated in a field-weakening (to enhance voltage sag ride-through) current controller for which design recommendations are presented
Improving security level of LTE authentication and key agreement procedure

In recent years, LTE (Long Term Evolution) has been one of the mainstreams of current wireless communication systems. But when its HSS authenticates UEs, the random number RAND generated by HSS for creating other keys during its delivery from HSS to UE is unencrypted. Also, many parameters are generated by invoking a function with only one input key, thus very easily to be cracked. So in this paper, we propose an improved approach in which the Diffie-Hellman algorithm is employed to solve the exposure problem of RAND in the authentication process, and an Pair key mechanism is deployed when creating other parameters, i.e., parameters are generated by invoking a function with at least two input keys. The purpose is increasing the security levels of all generated parameters so as to make LTE more secure than before.
A Discussion on IGBT Short-circuit Behavior and Fault Protection Schemes

AbstructIGBT’s are available with short-circuit withstand times approaching those of bipolar transistors. These IGBT’s can therefore be protected by the same relatively slow-acting circuitry. The more efficient IGBT’s, however, have lower shortcircuit withstand times. While protection of these types of IGBT’s is not difficult, it does require a reassessment of the traditional protection methods used for the bipolar transistors. An in-depth discussion on the behavior of IGBT’s under different short-circuit conditions is carried out and the effects of various parameters on permissible short-circuit time are analyzed. The paper also rethinks the problem of providing short-circuit protection in relation to the special characteristics of the most efficient IGBT’s. The pros and cons of some of the existing protection circuits are discussed and, based on the recommendations, a protection scheme is implemented to demonstrate that reliable short-circuit protection of these types of IGBT’s can be achieved without difficulty in a PWM motor-drive application.
Mal-Activity Diagrams for Capturing Attacks on Business Processes

Security is becoming an increasingly important issue for IT systems, yet it is often dealt with as separate from mainstream systems and software development and in many cases neglected or addressed post-hoc, yielding costly and unsatisfactory solutions. One idea to improve the focus on security might be to include such concerns into mainstream diagram notations used in information systems analysis, and one existing proposal for this is misuse cases, allowing for representation of attack use cases together with the normal legitimate use cases of a system. While this technique has shown much promise, it is not equally useful for all kinds of attack. In this paper we look into another type of technique that could complement misuse cases for early elicitation of security requirements, namely mal-activity diagrams. These allow the inclusion of hostile activities together with legitimate activities in business process models. Through some examples and a small case study, mal-activity diagrams are shown to have strengths in many aspects where misuse cases have weaknesses.
Exploring the patterns of social behavior in GitHub

Social coding paradigm is reshaping the distributed software development with a surprising speed in recent years. Github, a remarkable social coding community, attracts a huge number of developers in a short time. Various kinds of social networks are formed based on social activities among developers. Why this new paradigm can achieve such a great success in attracting external developers, and how they are connected in such a massive community, are interesting questions for revealing power of social coding paradigm. In this paper, we firstly compare the growth curves of project and user in GitHub with three traditional open source software communities to explore differences of their growth modes. We find an explosive growth of the users in GitHub and introduce the Diffusion of Innovation theory to illustrate intrinsic sociological basis of this phenomenon. Secondly, we construct follow-networks according to the follow behaviors among developers in GitHub. Finally, we present four typical social behavior patterns by mining follow-networks containing independence-pattern, group-pattern, star-pattern and hub-pattern. This study can provide several instructions of crowd collaboration to newcomers. According to the typical behavior patterns, the community manager could design corresponding assistive tools for developers.
Declarative and Nondeclarative Memory: Multiple Brain Systems Supporting Learning and Memory

The topic of multiple forms of memory is considered from a biological point of view. Fact-and-event (declarative, explicit) memory is contrasted with a collection of non conscious (non-declarative, implicit) memory abilities including skills and habits, priming, and simple conditioning. Recent evidence is reviewed indicating that declarative and non declarative forms of memory have different operating characteristics and depend on separate brain systems. A brain-systems framework for understanding memory phenomena is developed in light of lesion studies involving rats, monkeys, and humans, as well as recent studies with normal humans using the divided visual field technique, event-related potentials, and positron emission tomography (PET).
Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers

We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiple-structure data even in the presence of a large number of outliers. Our framework contains a novel scale estimator called Iterative Kth Ordered Scale Estimator (IKOSE). IKOSE can accurately estimate the scale of inliers for heavily corrupted multiple-structure data and is of interest by itself since it can be used in other robust estimators. In addition to IKOSE, our framework includes several original elements based on the weighting, clustering, and fusing of hypotheses. AKSWH can provide accurate estimates of the number of model instances and the parameters and the scale of each model instance simultaneously. We demonstrate good performance in practical applications such as line fitting, circle fitting, range image segmentation, homography estimation, and two--view-based motion segmentation, using both synthetic data and real images.
Arithmetic Coding for Data Compression

Arithmetic coding provides an e ective mechanism for removing redundancy in the encoding of data. We show how arithmetic coding works and describe an e cient implementation that uses table lookup as a fast alternative to arithmetic operations. The reduced-precision arithmetic has a provably negligible e ect on the amount of compression achieved. We can speed up the implementation further by use of parallel processing. We discuss the role of probability models and how they provide probability information to the arithmetic coder. We conclude with perspectives on the comparative advantages and disadvantages of arithmetic coding.