nils-herrmann/clean_scirepeval huggingface dataset:
Reconstruct argorithm of 2D barcode for reading the QR code on cylindrical surface

Traditional barcode recognition algorithm usually do not fit the cylindrical code but the one on flat surface. This paper proposes a low-cost approach to implement recognition of the curved QR codes printed on bottles or cans. Finder patterns are extracted from detecting module width proportion and corners of contours and an efficient direct least-square ellipse fitting method is employed to extract the elliptic edge and the boundary of code region. Then the code is reconstructed by direct mapping from the stereoscopic coordinates to the image plane using the 3D back-projection, thus the data of code could be restored. Compared with previous approaches, the proposed algorithm outperforms in not only the computation amount but also higher accuracy of the barcode recognition, whether in the flat or the cylindrical surface.
Application of Deep Belief Networks for opcode based malware detection

Deep belief nets (DBNs) have been successfully applied in various fields ranging from image classification and audio recognition to information retrieval. Compared with traditional shallow neural networks, DBNs can use unlabeled data to pretrain a multi-layer generative model, which can better solve the overfitting problem during training neural networks. In this study we represent malware as opcode sequences and use DBNs to detect malware. We compare the performance of DBNs with three widely used classification algorithms: Support Vector Machines (SVM), Decision Tree and k-Nearest Neighbor algorithm (KNN). The DBN model gives detection accuracy that is equal to the best of the other models. When using additional unlabeled data for DBN pre-training, DBNs performed better than the compared classification algorithms. We also use the DBNs as an autoencoder to extract the feature vectors of the input data. The experiments shows that the autoencoder can effectively model the underlying structure of the input data, and can significantly reduce the dimensions of feature vectors.
Evolving multimodal controllers with HyperNEAT

Natural brains effectively integrate multiple sensory modalities and act upon the world through multiple effector types. As researchers strive to evolve more sophisticated neural controllers, confronting the challenge of multimodality is becoming increasingly important. As a solution, this paper presents a principled new approach to exploiting indirect encoding to incorporate multimodality based on the HyperNEAT generative neuroevolution algorithm called the multi-spatial substrate (MSS). The main idea is to place each input and output modality on its own independent plane. That way, the spatial separation of such groupings provides HyperNEAT an a priori hint on which neurons are associated with which that can be exploited from the start of evolution. To validate this approach, the MSS is compared with more conventional approaches to HyperNEAT substrate design in a multiagent domain featuring three input and two output modalities. The new approach both significantly outperforms conventional approaches and reduces the creative burden on the user to design the layout of the substrate, thereby opening formerly prohibitive multimodal problems to neuroevolution.
Fa: A System for Automating Failure Diagnosis

Failures of Internet services and enterprise systems lead to user dissatisfaction and considerable loss of revenue. Since manual diagnosis is often laborious and slow, there is considerable interest in tools that can diagnose the cause of failures quickly and automatically from system-monitoring data. This paper identifies two key data-mining problems arising in a platform for automated diagnosis called {\em Fa}. Fa uses monitoring data to construct a database of{\em failure signatures} against which data from undiagnosed failures can be matched. Two novel challenges we address are to make signatures robust to the noisy monitoring data in production systems, and to generate reliable confidence estimates for matches. Fa uses a new technique called {\em anomaly-based clustering} when the signature database has no high-confidence match for an undiagnosed failure. This technique clusters monitoring data based on how it differs from the failure data, and pinpoints attributes linked to the failure. We show the effectiveness of Fa through a comprehensive experimental evaluation based on failures from a production setting, a variety of failures injected in a testbed, and synthetic data.
Self-ensembling for visual domain adaptation

This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant [29] of temporal ensembling , a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.
Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem

Adaptive beamforming methods are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated. In particular, if the desired signal is present in training snapshots, the adaptive array performance may be quite sensitive even to slight mismatches between the presumed and actual signal steering vectors (spatial signatures). Such mismatches can occur as a result of environmental nonstationarities, look direction errors, imperfect array calibration, distorted antenna shape, as well as distortions caused by medium inhomogeneities, near–far mismatch, source spreading, and local scattering. The similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch. Our approach is based on the optimization of worst-case performance. It turns out that the natural formulation of this adaptive beamforming problem involves minimization of a quadratic function subject to infinitely many nonconvex quadratic constraints. We show that this (originally intractable) problem can be reformulated in a convex form as the so-called second-order cone (SOC) program and solved efficiently (in polynomial time) using the well-established interior point method. It is also shown that the proposed technique can be interpreted in terms of diagonal loading where the optimal value of the diagonal loading factor is computed based on the known level of uncertainty of the signal steering vector. Computer simulations with several frequently encountered types of signal steering vector mismatches show better performance of our robust beamformer as compared with existing adaptive beamforming algorithms.
When Should Software Firms Commercialize New Products via Freemium Business Models ?

In the software industry, a challenge firms often face is how to effectively commercialize innovations. An emerging business model increasingly embraced by entrepreneurs, called freemium, combines “free” and “premium” consumption in association with a product or service. In a nutshell, this model involves giving away for free a certain level or type of consumption while making money on premium consumption. We develop a unifying multi-period microeconomic framework with network externalities embedded into consumer learning in order to capture the essence of conventional for-fee models, several key freemium business models such as feature-limited or time-limited, and uniform market seeding models. Under moderate informativeness of word-of-mouth signals, we fully characterize conditions under which firms prefer freemium models, depending on consumer priors on the value of individual software modules, perceptions of crossmodule synergies, and overall value distribution across modules. Within our framework, we show that uniform seeding is always dominated by either freemium models or conventional for-fee models. We further discuss managerial and policy implications based on our analysis. Interestingly, we show that freemium, in one form or another, is always preferred from the social welfare perspective, and we provide guidance on when the firms need to be incentivized to align their interests with the society’s. Finally, we discuss how relaxing some of the assumptions of our model regarding costs or informativeness and heterogeneity of word of mouth may reduce the profit gap between seeding and the other models, and potentially lead to seeding becoming the preferred approach for the firm.
Neural Tensor Networks with Diagonal Slice Matrices

Although neural tensor networks (NTNs) have been successful in many natural language processing tasks, they require a large number of parameters to be estimated, which often results in overfitting and long training times. We address these issues by applying eigendecomposition to each slice matrix of a tensor to reduce the number of parameters. We evaluate our proposed NTN models in two tasks. First, the proposed models are evaluated in a knowledge graph completion task. Second, a recursive NTN (RNTN) extension of the proposed models is evaluated on a logical reasoning task. The experimental results show that our proposed models learn better and faster than the original (R)NTNs.
Evolving grounded communication for robots

The computational and robotic synthesis of language evolution is emerging as a new exciting field of research. The objective is to come up with precise operational models of how communities of agents, equipped with a cognitive apparatus, a sensori-motor system, and a body, can arrive at shared grounded communication systems. Such systems may have similar characteristics to animal communication or human language. Apart from its technological interest in building novel applications in the domain of human-robot or robot-robot interaction, this research is of interest to the many disciplines concerned with the origins and evolution of language and communication.
A Brief History of Microprogramming

A processing unit in a computer system is composed of a data path and a control unit. The data path includes registers, function units such as ALUs (arithmetic and logic units) and shifters, interface units for main memory and/or I/O busses, and internal processor busses. The control unit governs the series of steps taken by the data path during the execution of a user-visible instruction, or macroinstruction (e.g., load, add, store, conditional branch).
Language model verbalization for automatic speech recognition

Transcribing speech in properly formatted written language presents some challenges for automatic speech recognition systems. The difficulty arises from the conversion ambiguity between verbal and written language in both directions. Non-lexical vocabulary items such as numeric entities, dates, times, abbreviations and acronyms are particularly ambiguous. This paper describes a finite-state transducer based approach that improves proper transcription of these entities. The approach involves training a language model in the written language domain, and integrating verbal expansions of vocabulary items as a finite-state model into the decoding graph construction. We build an inverted finite-state transducer to map written vocabulary items to alternate verbal expansions using rewrite rules. Then, this verbalizer transducer is composed with the n-gram language model to obtain a verbalized language model, whose input labels are in the verbal language domain while output labels are in the written language domain. We show that the proposed approach is very effective in improving the recognition accuracy of numeric entities.
Towards a quantum World Wide Web

We elaborate a quantum model for corpora of written documents, like the pages forming the World Wide Web. To that end, we are guided by how physicists constructed quantum theory for microscopic entities, which unlike classical objects cannot be fully represented in our spatial theater. We suggest that a similar construction needs to be carried out by linguists and computational scientists, to capture the full meaning content of collections of documental entities. More precisely, we show how to associate a quantum-like ‘entity of meaning’ to a ‘language entity formed by printed documents’, considering the latter as the collection of traces that are left by the former, in specific results of search actions that we describe as measurements. In other words, we offer a perspective where a collection of documents, like the Web, is described as the space of manifestation of a more complex entity – the QWeb – which is the object of our modeling, drawing its inspiration from previous studies on operational-realistic approaches to quantum physics and quantum modeling of human cognition and decision-making. We emphasize that a consistent QWeb model needs to account for the observed correlations between words appearing in printed documents, e.g., co-occurrences, as the latter would depend on the ‘meaning connections’ existing between the concepts that are associated with these words. In that respect, we show that both ‘context and interference (quantum) effects’ are required to explain the probabilities calculated by counting the relative number of documents containing certain words and co-ocurrrences of words.
MAFRA - A MApping FRAmework for Distributed Ontologies

Ontologies as means for conceptualizing and structuring domain knowledge within a community of interest are seen as a key to realize the Semantic Web vision. However, the decentralized nature of the Web makes achieving this consensus across communities difficult, thus, hampering efficient knowledge sharing between them. In order to balance the autonomy of each community with the need for interoperability, mapping mechanisms between distributed ontologies in the Semantic Web are required. In this paper we present MAFRA, an interactive, incremental and dynamic framework for mapping distributed ontologies.
Modular inverse reinforcement learning for visuomotor behavior

In a large variety of situations one would like to have an expressive and accurate model of observed animal or human behavior. While general purpose mathematical models may capture successfully properties of observed behavior, it is desirable to root models in biological facts. Because of ample empirical evidence for reward-based learning in visuomotor tasks, we use a computational model based on the assumption that the observed agent is balancing the costs and benefits of its behavior to meet its goals. This leads to using the framework of reinforcement learning, which additionally provides well-established algorithms for learning of visuomotor task solutions. To quantify the agent’s goals as rewards implicit in the observed behavior, we propose to use inverse reinforcement learning, which quantifies the agent’s goals as rewards implicit in the observed behavior. Based on the assumption of a modular cognitive architecture, we introduce a modular inverse reinforcement learning algorithm that estimates the relative reward contributions of the component tasks in navigation, consisting of following a path while avoiding obstacles and approaching targets. It is shown how to recover the component reward weights for individual tasks and that variability in observed trajectories can be explained succinctly through behavioral goals. It is demonstrated through simulations that good estimates can be obtained already with modest amounts of observation data, which in turn allows the prediction of behavior in novel configurations.
Multimodal Multi-task Learning for Dimensional and Continuous Emotion Recognition

Automatic emotion recognition is a challenging task which can make great impact on improving natural human computer interactions. In this paper, we present our effort for the Affect Subtask in the Audio/Visual Emotion Challenge (AVEC) 2017, which requires participants to perform continuous emotion prediction on three affective dimensions: Arousal, Valence and Likability based on the audiovisual signals. We highlight three aspects of our solutions: 1) we explore and fuse different hand-crafted and deep learned features from all available modalities including acoustic, visual, and textual modalities, and we further consider the interlocutor influence for the acoustic features; 2) we compare the effectiveness of non-temporal model SVR and temporal model LSTM-RNN and show that the LSTM-RNN can not only alleviate the feature engineering efforts such as construction of contextual features and feature delay, but also improve the recognition performance significantly; 3) we apply multi-task learning strategy for collaborative prediction of multiple emotion dimensions with shared representations according to the fact that different emotion dimensions are correlated with each other. Our solutions achieve the CCC of 0.675, 0.756 and 0.509 on arousal, valence, and likability respectively on the challenge testing set, which outperforms the baseline system with corresponding CCC of 0.375, 0.466, and 0.246 on arousal, valence, and likability.
Venus: verification for untrusted cloud storage

This paper presents Venus, a service for securing user interaction with untrusted cloud storage. Specifically, Venus guarantees integrity and consistency for applications accessing a key-based object store service, without requiring trusted components or changes to the storage provider. Venus completes all operations optimistically, guaranteeing data integrity. It then verifies operation consistency and notifies the application. Whenever either integrity or consistency is violated, Venus alerts the application. We implemented Venus and evaluated it with Amazon S3 commodity storage service. The evaluation shows that it adds no noticeable overhead to storage operations.
A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment

We tackle a task where an agent learns to navigate in a 2D maze-like environment called XWORLD. In each session, the agent perceives a sequence of raw-pixel frames, a natural language command issued by a teacher, and a set of rewards. The agent learns the teacher’s language from scratch in a grounded and compositional manner, such that after training it is able to correctly execute zero-shot commands: 1) the combination of words in the command never appeared before, and/or 2) the command contains new object concepts that are learned from another task but never learned from navigation. Our deep framework for the agent is trained end to end: it learns simultaneously the visual representations of the environment, the syntax and semantics of the language, and the action module that outputs actions. The zero-shot learning capability of our framework results from its compositionality and modularity with parameter tying. We visualize the intermediate outputs of the framework, demonstrating that the agent truly understands how to solve the problem. We believe that our results provide some preliminary insights on how to train an agent with similar abilities in a 3D environment.
Securing Computer Hardware Using 3D Integrated Circuit (IC) Technology and Split Manufacturing for Obfuscation

The fabrication of digital Integrated Circuits (ICs) is increasingly outsourced. Given this trend, security is recognized as an important issue. The threat agent is an attacker at the IC foundry that has information about the circuit and inserts covert, malicious circuitry. The use of 3D IC technology has been suggested as a possible technique to counter this threat. However, to our knowledge, there is no prior work on how such technology can be used effectively. We propose a way to use 3D IC technology for security in this context. Specifically, we obfuscate the circuit by lifting wires to a trusted tier, which is fabricated separately. This is referred to as split manufacturing. For this setting, we provide a precise notion of security, that we call k-security, and a characterization of the underlying computational problems and their complexity. We further propose a concrete approach for identifying sets of wires to be lifted, and the corresponding security they provide. We conclude with a comprehensive empirical assessment with benchmark circuits that highlights the security versus cost trade-offs introduced by 3D IC based circuit obfuscation.
Single Bit-Line 7T SRAM Cell for Near-Threshold Voltage Operation With Enhanced Performance and Energy in 14 nm FinFET Technology

Although near-threshold voltage (NTV) operation is an attractive means of achieving high energy efficiency, it can degrade the circuit stability of static random access memory (SRAM) cells. This paper proposes an NTV 7T SRAM cell in a 14 nm FinFET technology to eliminate read disturbance by disconnecting the path from the bit-line to the cross-coupled inverter pair using the transmission gate. In the proposed 7T SRAM cell, the half-select issue is resolved, meaning that no write-back operation is required. A folded-column structure is applied to the proposed 7T SRAM cell to reduce the read access time and energy consumption. To reduce the standby power, the proposed 7T SRAM cell uses only a single bit-line for both read and write operations. To achieve proper “1” writing operation with a single bit-line, a two-phase approach is proposed. Compared to the conventional 8T SRAM cell, the proposed 7T SRAM cell improves the read access time, energy, and standby power by 13%, 42%, and 23%, respectively, with a 3% smaller cell area.
Fine motor skills and early comprehension of the world: two new school readiness indicators.

Duncan et al. (2007) presented a new methodology for identifying kindergarten readiness factors and quantifying their importance by determining which of children's developing skills measured around kindergarten entrance would predict later reading and math achievement. This article extends Duncan et al.'s work to identify kindergarten readiness factors with 6 longitudinal data sets. Their results identified kindergarten math and reading readiness and attention as the primary long-term predictors but found no effects from social skills or internalizing and externalizing behavior. We incorporated motor skills measures from 3 of the data sets and found that fine motor skills are an additional strong predictor of later achievement. Using one of the data sets, we also predicted later science scores and incorporated an additional early test of general knowledge of the social and physical world as a predictor. We found that the test of general knowledge was by far the strongest predictor of science and reading and also contributed significantly to predicting later math, making the content of this test another important kindergarten readiness indicator. Together, attention, fine motor skills, and general knowledge are much stronger overall predictors of later math, reading, and science scores than early math and reading scores alone.