nils-herrmann/clean_scirepeval huggingface dataset:
Discriminative K-SVD for dictionary learning in face recognition

In a sparse-representation-based face recognition scheme, the desired dictionary should have good representational power (i.e., being able to span the subspace of all faces) while supporting optimal discrimination of the classes (i.e., different human subjects). We propose a method to learn an over-complete dictionary that attempts to simultaneously achieve the above two goals. The proposed method, discriminative K-SVD (D-KSVD), is based on extending the K-SVD algorithm by incorporating the classification error into the objective function, thus allowing the performance of a linear classifier and the representational power of the dictionary being considered at the same time by the same optimization procedure. The D-KSVD algorithm finds the dictionary and solves for the classifier using a procedure derived from the K-SVD algorithm, which has proven efficiency and performance. This is in contrast to most existing work that relies on iteratively solving sub-problems with the hope of achieving the global optimal through iterative approximation. We evaluate the proposed method using two commonly-used face databases, the Extended YaleB database and the AR database, with detailed comparison to 3 alternative approaches, including the leading state-of-the-art in the literature. The experiments show that the proposed method outperforms these competing methods in most of the cases. Further, using Fisher criterion and dictionary incoherence, we also show that the learned dictionary and the corresponding classifier are indeed better-posed to support sparse-representation-based recognition.
Repairing Programs with Semantic Code Search (T)

Automated program repair can potentially reduce debugging costs and improve software quality but recent studies have drawn attention to shortcomings in the quality of automatically generated repairs. We propose a new kind of repair that uses the large body of existing open-source code to find potential fixes. The key challenges lie in efficiently finding code semantically similar (but not identical) to defective code and then appropriately integrating that code into a buggy program. We present SearchRepair, a repair technique that addresses these challenges by(1) encoding a large database of human-written code fragments as SMT constraints on input-output behavior, (2) localizing a given defect to likely buggy program fragments and deriving the desired input-output behavior for code to replace those fragments, (3) using state-of-the-art constraint solvers to search the database for fragments that satisfy that desired behavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program testsuites. We find that SearchRepair repairs 150 (19%) of 778 benchmark C defects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by the four techniques by measuring how many independent, not-used-during-repairtests they pass, and find that SearchRepair-repaired programs pass 97.3% ofthe tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repaired programs pass 68.7%, 72.1%, and 64.2% of the tests, respectively. We concludethat SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects those tools cannot.
Coherent-array imaging using phased subarrays. Part I: basic principles

The front-end hardware complexity of a coherent array imaging system scales with the number of active array elements that are simultaneously used for transmission or reception of signals. Different imaging methods use different numbers of active channels and data collection strategies. Conventional full phased array (FPA) imaging produces the best image quality using all elements for both transmission and reception, and it has high front-end hardware complexity. In contrast, classical synthetic aperture (CSA) imaging only transmits on and receives from a single element at a time, minimizing the hardware complexity but achieving poor image quality. We propose a new coherent array imaging method - phased subarray (PSA) imaging - that performs partial transmit and receive beam-forming using a subset of adjacent elements at each firing step. This method reduces the number of active channels to the number of subarray elements; these channels are multiplexed across the full array and a reduced number of beams are acquired from each subarray. The low-resolution subarray images are laterally upsampled, interpolated, weighted, and coherently summed to form the final high-resolution PSA image. The PSA imaging reduces the complexity of the front-end hardware while achieving image quality approaching that of FPA imaging
Malware Detection with Deep Neural Network Using Process Behavior

Increase of malware and advanced cyber-attacks are now becoming a serious problem. Unknown malware which has not determined by security vendors is often used in these attacks, and it is becoming difficult to protect terminals from their infection. Therefore, a countermeasure for after infection is required. There are some malware infection detection methods which focus on the traffic data comes from malware. However, it is difficult to perfectly detect infection only using traffic data because it imitates benign traffic. In this paper, we propose malware process detection method based on process behavior in possible infected terminals. In proposal, we investigated stepwise application of Deep Neural Networks to classify malware process. First, we train the Recurrent Neural Network (RNN) to extract features of process behavior. Second, we train the Convolutional Neural Network (CNN) to classify feature images which are generated by the extracted features from the trained RNN. The evaluation result in several image size by comparing the AUC of obtained ROC curves and we obtained AUC= 0:96 in best case.
Lane detection algorithm using vanishing point

This paper presents a lane detection method to screen data using the vanishing point according to the perspective feature of the camera. The line data is obtained after Canny and Hough transform of the raw image. The filter conditions are created according to the vanishing point and other location features. The algorithm saves the detected lane and vanishing points in near history. The algorithm clusters and integrates to determine the detection output according to the historical data. Finally, according to the output, a new vanishing point is fitted for the next circuit. The experiments indicate that the algorithm detects the lane accurately and is of parameters self-adaptability and robustness, while improving the utilization of the internal data.
Tunable Dual-Band Antennas for 0.7–1.1-GHz and 1.7–2.3-GHz Carrier Aggregation Systems

Single- and dual-feed, dual-frequency, low-profile antennas with independent tuning using varactor diodes have been demonstrated. The dual-feed planar inverted F-antenna (PIFA) has two operating frequencies which can be independently tuned from 0.7 to 1.1 GHz and from 1.7 to 2.3 GHz with better than -10 dB impedance match. The isolation between the high-band and the low-band ports is >13 dB; hence, one resonant frequency can be tuned without affecting the other. The single-feed antenna has two resonant frequencies, which can be independently tuned from 1.2 to 1.6 GHz and from 1.6 to 2.3 GHz with better than -10 dB impedance match for most of the tuning range. The tuning is done using varactor diodes with a capacitance range from 0.8 to 3.8 pF, which is compatible with RF MEMS devices. The antenna volumes are 63 × 100 × 3.15 mm3 on er = 3.55 substrates and the measured antenna efficiencies vary between 25% and 50% over the tuning range. The application areas are in carrier aggregation systems for fourth generation (4G) wireless systems.
Speech Emotion Recognition with Data Augmentation and Layer-wise Learning Rate Adjustment

In this work, we design a neural network for recognizing emotions in speech, using the standard IEMOCAP dataset. Following the latest advances in audio analysis, we use an architecture involving both convolutional layers, for extracting highlevel features from raw spectrograms, and recurrent ones for aggregating long-term dependencies. Applying techniques of data augmentation, layerwise learning rate adjustment and batch normalization, we obtain highly competitive results, with 64.5% weighted accuracy and 61.7% unweighted accuracy on four emotions. Moreover, we show that the model performance is strongly correlated with the labeling confidence, which highlights a fundamental difficulty in emotion recognition.
Image Classification Using Generative Neuro Evolution for Deep Learning

Research into deep learning has demonstrated performance competitive with humans on some visual tasks, however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus neuroevolution for deep learning is investigated in this paper. In particular, the Hypercube-based Neuro Evolution of Augmenting Topologies is a NE approach that can effectively learn large neural structures by training an indirect encoding that compresses the artificial neural network (ANN) weight pattern as a function of geometry. The methodologies are tested on a traditional image classification task as well as one tailored to overhead satellite imagery. The results show that Hyper NEAT struggles with performing image classification by itself, but can be effective in training a feature extractor that other ML approaches can learn from.
Comparative Analysis of Static and Dynamic CMOS Logic Design

The choice of the CMOS logic to be used for implementation of a given specification is usually dependent on the optimization and the performance constraints that the finished chip is required to meet. This paper presents a comparative study of CMOS static and dynamic logic. Effect of voltage variation on power and delay of static and dynamic CMOS logic styles studied. The performance of static logic is better than dynamic logic for designing basic logic gates like NAND and NOR. But the dynamic casecode voltage switch logic (CVSL) achieves better performance. 75% lesser power delay product is achieved than that of static CVSL. However, it observed that dynamic logic performance is better for higher fan in and complex logic circuits.
Effects of Bonding Parameters on the Reliability of Fine-Pitch Cu/Ni/SnAg Micro-Bump Chip-to-Chip Interconnection for Three-Dimensional Chip Stacking

As the demands for portable electronic products increase, through-silicon-via (TSV)-based three-dimensional integrated-circuit (3-D IC) integration is becoming increasingly important. Micro-bump-bonded interconnection is one approach that has great potential to meet this requirement. In this paper, a 30-μm pitch chip-to-chip (C2C) interconnection with Cu/Ni/SnAg micro bumps was assembled using the gap-controllable thermal bonding method. The bonding parameters were evaluated by considering the variation in the contact resistance after bonding. The effects of the bonding time and temperature on the IMC thickness of the fabricated C2C interconnects are also investigated to determine the correlation between its thickness and reliability performance. The reliability of the C2C interconnects with the selected underfill was studied by performing a -55°C- 125°C temperature cycling test (TCT) for 2000 cycles and a 150°C high-temperature storage (HTS) test for 2000 h. The interfaces of the failed samples in the TCT and HTS tests are then inspected by scanning electron microscopy (SEM), which is utilized to obtain cross-sectional images. To validate the experimental results, finite-element (FE) analysis is also conducted to elucidate the interconnect reliability of the C2C interconnection. Results show that consistent bonding quality and stable contact resistance of the fine-pitch C2C interconnection with the micro bumps were achieved by giving the appropriate choice of the bonding parameters, and those bonded joints can thus serve as reliable interconnects for use in 3-D chip stacking.
Adolescent development.

This chapter identifies the most robust conclusions and ideas about adolescent development and psychological functioning that have emerged since Petersen's 1988 review. We begin with a discussion of topics that have dominated recent research, including adolescent problem behavior, parent-adolescent relations, puberty, the development of the self, and peer relations. We then identify and examine what seem to us to be the most important new directions that have come to the fore in the last decade, including research on diverse populations, contextual influences on development, behavioral genetics, and siblings. We conclude with a series of recommendations for future research on adolescence.
Programming in the wild: trends in youth computational participation in the online scratch community

Most research in primary and secondary computing education has focused on understanding learners within formal classroom communities, leaving aside the growing number of promising informal online programming communities where young learners contribute, comment, and collaborate on programs. In this paper, we examined trends in computational participation in Scratch, an online community with over 1 million registered youth designers primarily 11-18 years of age. Drawing on a random sample of 5,000 youth programmers and their activities over three months in early 2012, we examined the quantity of programming concepts used in projects in relation to level of participation, gender, and account age of Scratch programmers. Latent class analyses revealed four unique groups of programmers. While there was no significant link between level of online participation, ranging from low to high, and level of programming sophistication, the exception was a small group of highly engaged users who were most likely to use more complex programming concepts. Groups who only used few of the more sophisticated programming concepts, such as Booleans, variables and operators, were identified as Scratch users new to the site and girls. In the discussion we address the challenges of analyzing young learners' programming in informal online communities and opportunities for designing more equitable computational participation.
Seawater desalination using renewable energy sources

The origin and continuation of mankind is based on water. Water is one of the most abundant resources on earth, covering three-fourths of the planet’s surface. However, about 97% of the earth’s water is salt water in the oceans, and a tiny 3% is fresh water. This small percentage of the earth’s water—which supplies most of human and animal needs—exists in ground water, lakes and rivers. The only nearly inexhaustible sources of water are the oceans, which, however, are of high salinity. It would be feasible to address the water-shortage problem with seawater desalination; however, the separation of salts from seawater requires large amounts of energy which, when produced from fossil fuels, can cause harm to the environment. Therefore, there is a need to employ environmentally-friendly energy sources in order to desalinate seawater. After a historical introduction into desalination, this paper covers a large variety of systems used to convert seawater into fresh water suitable for human use. It also covers a variety of systems, which can be used to harness renewable energy sources; these include solar collectors, photovoltaics, solar ponds and geothermal energy. Both direct and indirect collection systems are included. The representative example of direct collection systems is the solar still. Indirect collection systems employ two subsystems; one for the collection of renewable energy and one for desalination. For this purpose, standard renewable energy and desalination systems are most often employed. Only industrially-tested desalination systems are included in this paper and they comprise the phase change processes, which include the multistage flash, multiple effect boiling and vapour compression and membrane processes, which include reverse osmosis and electrodialysis. The paper also includes a review of various systems that use renewable energy sources for desalination. Finally, some general guidelines are given for selection of desalination and renewable energy systems and the parameters that need to be considered. q 2005
Sharing Bandwidth by Allocating Switch Buffer in Data Center Networks

In today's data centers, the round trip propagation delay is quite small. Therefore, switch buffer sizes are much larger than the Bandwidth Delay Product (BDP). Based on this observation, in this paper we introduce a new transport protocol which provides bandwidth Sharing by Allocating switch Buffer (SAB) for data centers. SAB sets the congestion windows for flows based on the buffer size of the switches along the path. On one hand, as long as the total buffer allocated to all the flows is larger than the BDP, the network bandwidth can be fully utilized. On the other hand, since SAB only allocates the buffer space to flows, the totally injected traffic will not exceed the network capacity. Thus, SAB rarely loses packets. SAB also reduces flow completion time by allowing flows to reach their fair share of bandwidth quickly. The results of a series of experiments and simulations demonstrate that SAB has the features of fast convergence and rare packet loss. It reduces the latency of short flows and solves theTCP Incast and TCP Outcast problems.
Task and user effects on reading patterns in information search

0953-5438/$ see front matter 2011  A doi:10.1016/j.intcom.2011.04.007 ⇑ Corresponding author. E-mail addresses: m.cole@rutgers.edu (M.J. Co (J. Gwizdka), changl@eden.rutgers.edu (C. Liu), ralf@b rutgers.edu (N.J. Belkin), xiangminz@gmail.com (X. Zh We report on an investigation into people’s behaviors on information search tasks, specifically the relation between eye movement patterns and task characteristics. We conducted two independent user studies (n = 32 and n = 40), one with journalism tasks and the other with genomics tasks. The tasks were constructed to represent information needs of these two different users groups and to vary in several dimensions according to a task classification scheme. For each participant we classified eye gaze data to construct models of their reading patterns. The reading models were analyzed with respect to the effect of task types and Web page types on reading eye movement patterns. We report on relationships between tasks and individual reading behaviors at the task and page level. Specifically we show that transitions between scanning and reading behavior in eye movement patterns and the amount of text processed may be an implicit indicator of the current task type facets. This may be useful in building user and task models that can be useful in personalization of information systems and so address design demands driven by increasingly complex user actions with information systems. One of the contributions of this research is a new methodology to model information search behavior and investigate information acquisition and cognitive processing in interactive information tasks.
Towards Research Collaboration - a Taxonomy of Social Research Network Sites

The increase of scientific collaboration coincides with the technological and social advancement of social software applications which can change the way we research. Among social software, social network sites have recently gained immense popularity in a hedonic context. This paper focuses on social network sites as an emerging application designed for the specific needs of researchers. To give an overview about these sites we use a data set of 24 case studies and in-depth interviews with the founders of ten social research network sites. The gathered data leads to a first tentative taxonomy and to a definition of SRNS identifying four basic functionalities identity and network management, communication, information management, and collaboration. The sites in the sample correspond to one of the following four types: research directory sites, research awareness sites, research management sites and research collaboration sites. These results conclude with implications for providers of social research network sites.
Fused-layer CNN accelerators

Deep convolutional neural networks (CNNs) are rapidly becoming the dominant approach to computer vision and a major component of many other pervasive machine learning tasks, such as speech recognition, natural language processing, and fraud detection. As a result, accelerators for efficiently evaluating CNNs are rapidly growing in popularity. The conventional approaches to designing such CNN accelerators is to focus on creating accelerators to iteratively process the CNN layers. However, by processing each layer to completion, the accelerator designs must use off-chip memory to store intermediate data between layers, because the intermediate data are too large to fit on chip. In this work, we observe that a previously unexplored dimension exists in the design space of CNN accelerators that focuses on the dataflow across convolutional layers. We find that we are able to fuse the processing of multiple CNN layers by modifying the order in which the input data are brought on chip, enabling caching of intermediate data between the evaluation of adjacent CNN layers. We demonstrate the effectiveness of our approach by constructing a fused-layer CNN accelerator for the first five convolutional layers of the VGGNet-E network and comparing it to the state-of-the-art accelerator implemented on a Xilinx Virtex-7 FPGA. We find that, by using 362KB of on-chip storage, our fused-layer accelerator minimizes off-chip feature map data transfer, reducing the total transfer by 95%, from 77MB down to 3.6MB per image.
Interactive and Deterministic Data Cleaning

We present Falcon, an interactive, deterministic, and declarative data cleaning system, which uses SQL update queries as the language to repair data. Falcon does not rely on the existence of a set of pre-defined data quality rules. On the contrary, it encourages users to explore the data, identify possible problems, and make updates to fix them. Bootstrapped by one user update, Falcon guesses a set of possible sql update queries that can be used to repair the data. The main technical challenge addressed in this paper consists in finding a set of sql update queries that is minimal in size and at the same time fixes the largest number of errors in the data. We formalize this problem as a search in a lattice-shaped space. To guarantee that the chosen updates are semantically correct, Falcon navigates the lattice by interacting with users to gradually validate the set of sql update queries. Besides using traditional one-hop based traverse algorithms (e.g., BFS or DFS), we describe novel multi-hop search algorithms such that Falcon can dive over the lattice and conduct the search efficiently. Our novel search strategy is coupled with a number of optimization techniques to further prune the search space and efficiently maintain the lattice. We have conducted extensive experiments using both real-world and synthetic datasets to show that Falcon can effectively communicate with users in data repairing.
An EMI robust LIN driver with low electromagnetic emission

This paper describes a LIN (Local Interconnect Network) Transmitter designed in a BCD HV technology. The key design target is to comply with EMI (electromagnetic interference) specification limits. The two main aspects are low EME (electromagnetic emission) and sufficient immunity against RF disturbance. A gate driver is proposed which uses a certain current summation network for lowering the slew rate on the one hand and being reliable against radio frequency (RF) disturbances within the automotive environment on the other hand. Nowadays the low cost single wire LIN Bus is used for establishing communication between sensors, actuators and other components.