nils-herrmann/clean_scirepeval huggingface dataset:
NONCOMMUTATIVE GEOMETRY AND THE RIEMANN ZETA FUNCTION

According to my first teacher Gustave Choquet one does, by openly facing a well known unsolved problem, run the risk of being remembered more by one’s failure than anything else. After reaching a certain age, I realized that waiting “safely” until one reaches the end-point of one’s life is an equally selfdefeating alternative. In this paper I shall first look back at my early work on the classification of von Neumann algebras and cast it in the unusual light of André Weil’s Basic Number Theory. I shall then explain that this leads to a natural spectral interpretation of the zeros of the Riemann zeta function and a geometric framework in which the Frobenius, its eigenvalues and the Lefschetz formula interpretation of the explicit formulas continue to hold even for number fields. We shall then prove the positivity of the Weil distribution assuming the validity of the analogue of the Selberg trace formula. The latter remains unproved and is equivalent to RH for all L-functions with Grössencharakter.
DualRadviz: Preserving Context between Classification Evaluation and Data Exploration with RadViz

With today's flood of data coming from many types of sources, Machine Learning becomes increasingly important. Though, many times the use of Machine Learning is not enough to make sense of all this data. This makes visualization a very useful tool for Machine Learning practitioners and data analysts alike. Interactive visualization techniques can be very helpful by giving insight on the meaning of the output from classification tasks. This also applies to the data itself, as visualization can make some characteristics of the data become clear. Several bi-dimensional projection methods have been used to visualize data instances based on their attribute values. This visualization is more difficult when the instances have a large number of attributes. One of the visualization techniques that can deal with high dimensional data is Radial Coordinates Visualization (RadViz). RadViz can also be employed to visualize the performance of a probabilistic classifier, helping a user to find problematic instances that might have been misclassified. In this study, a new approach to use RadViz is proposed and investigated. The proposed approach combines the two aforementioned uses of RadViz (attribute-based data exploration and result exploration based on the output of probabilistic classification). For such, it approach provides an easy transition between the two types of visualization. This allows the context to be preserved, since the user can visually track the same data instance from one type of visualization to the other. In order to evaluate the proposed approach, a prototype, named DualRadviz, was implemented. On this prototype, in addition to RadViz, visualization by Parallel Coordinates is also provided, so that precise instance inspection can be performed, since, different from RadViz, Parallel coordinates visualization does not suffer from ambiguity. To illustrate the usefulness of the proposed method, a case study is presented.
Broadband printed slot antenna for the fifth generation (5G) mobile and wireless communications

In this paper, a broadband elliptical-shaped slot antenna for the future fifth generation (5G) wireless applications is proposed. The antenna has a compact size of 0.5λ0 × 0.5λ0 at 30 GHz. It consists of a circular shaped radiating patch fed by a 50-Ω microstrip line via proximity-feed technique. An elliptically shaped slot is etched in the ground plane to enhance the antenna bandwidth. A stub has been added to the microstrip line feed to achieve better impedance matching bandwidth of the antenna. Simulated results indicate that the proposed 5G antenna yields a broadband impedance bandwidth larger than 67% (from 20 GHz to beyond 40 GHz) for S11 less than -10 dB. The achieved bandwidth covers both future 5G bands (28/38 GHz). The proposed antenna provides almost omni-directional patterns, relatively flat gain, and high radiation efficiency through the frequency band excluding the rejected band.
An 80 mV Startup Dual-Mode Boost Converter by Charge-Pumped Pulse Generator and Threshold Voltage Tuned Oscillator With Hot Carrier Injection

This paper presents an 80 mV startup-voltage dual-mode boost converter for energy harvesting applications. The charge-pumped pulse generator enables a startup operation of the boost converter from the input voltage of 80 mV. The threshold voltage tuned oscillator for the clock generator of the boost converter compensates for the die-to-die process variation by a hot carrier injection (HCI), thereby reducing the minimum operation voltage (VDDMIN) of the clock generator by 45% with a trimming time of 10 minutes. The proposed step-up converter achieves the lowest startup voltage without using a mechanical switch or a large transformer.
Goal-oriented requirements engineering: an extended systematic mapping study

Over the last two decades, much attention has been paid to the area of goal-oriented requirements engineering (GORE), where goals are used as a useful conceptualization to elicit, model, and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within requirements engineering (RE) and beyond, such as agent orientation, aspect orientation, business intelligence, model-driven development, and security. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. In this work, we present a systematic mapping study, covering the 246 top-cited GORE-related conference and journal papers, according to Scopus. Our literature map addresses several research questions: we classify the types of papers (e.g., proposals, formalizations, meta-studies), look at the presence of evaluation, the topics covered (e.g., security, agents, scenarios), frameworks used, venues, citations, author networks, and overall publication numbers. For most questions, we evaluate trends over time. Our findings show a proliferation of papers with new ideas and few citations, with a small number of authors and papers dominating citations; however, there is a slight rise in papers which build upon past work (implementations, integrations, and extensions). We see a rise in papers concerning adaptation/variability/evolution and a slight rise in case studies. Overall, interest in GORE has increased. We use our analysis results to make recommendations concerning future GORE research and make our data publicly available.
On Real-Time Transactions

Next generation real-time systems will require greater flexibility and predictability than is commonly found in today's systems. These future systems include the space station, integrated vision/robotics/AI systems, collections of humans/robots coordinating to achieve common objectives (usually in hazardous environments such as undersea exploration or chemical plants), and various command and control applications. The complexity of such systems due to timing constraints, concurrency, and distribution is high. It is accepted that the synchronization, failure atomicity, and permanence properties of transactions aid in the development of distributed systems. However, little work has been done in exploiting transactions in a real-time context. We have been attempting to categorize real-time data into classes depending on their time, synchronization, atomicity, and permanence properties. Then, using the semantics of the data and the applications, we are developing special, tailored, real-time transactions that only supply the minimal properties necessary for that class. This reduces the system overhead in supporting access to various types of data. The eventual goal is to verify that timing requirements can be met.
Designing Relational Agents as Long Term Social Companions for Older Adults

Older adults with strong social connections are at a reduced risk for health problems and mortality. We describe two field studies to inform the development of a virtual agent designed to provide long-term, continuous social support to isolated older adults. Findings include the topics that older adults would like to discuss with a companion agent, in addition to overall reactions to interacting with a remote-controlled companion agent installed in their home for a week. Results indicate a generally positive attitude towards companion agents and a rich research agenda for virtual companion agents.
[Episodic memory: from mind to brain].

Episodic memory is a neurocognitive (brain/mind) system, uniquely different from other memory systems, that enables human beings to remember past experiences. The notion of episodic memory was first proposed some 30 Years ago. At that time it was defined in terms of materials and tasks. It was subsequently refined and elaborated in terms of ideas such as self, subjective time, and autonoetic consciousness. This chapter provides a brief history of the concept of episodic memory, describes how it has changed (indeed greatly changed) since its inception, considers criticisms of it, and then discusses supporting evidence provided by (a) neuropsychological studies of patterns of memory impairment caused by brain damage, and (b) functional neuroimaging studies of patterns of brain activity of normal subjects engaged in various memory tasks. I also suggest that episodic memory is a true, even if as yet generally unappreciated, marvel of nature.
An online PPGI approach for camera based heart rate monitoring using beat-to-beat detection

In this work an online method for camera-based heart rate detection, also known as Photoplethysmography Imaging (PPGI), is presented. The pulse related signal is obtained from RGB videos of the human face, using an off the shelf camera under ambient light conditions. The algorithm for heart rate estimation is based on the beat-to-beat analysis of the PPGI signal, allowing the estimation of psychophysiological parameters like the heart rate variability. The system is tested indoors under laboratory conditions, and in field trials integrated in a car. Field tests are done as a step towards robust appraisal of psycho-physiological driver states. In measurements with eight subjects, a sensitivity of ∼93% for pulse peak detection is reached under resting and constant light conditions. First test drives show good results for heart rate estimation while driving on highways outside of city traffic. The results indicate that PPGI can be a suitable solution for providing vital sign information of drivers in cars.
Compressing deep neural networks using a rank-constrained topology

We present a general approach to reduce the size of feedforward deep neural networks (DNNs). We propose a rankconstrained topology, which factors the weights in the input layer of the DNN in terms of a low-rank representation: unlike previous work, our technique is applied at the level of the filters learned at individual hidden layer nodes, and exploits the natural two-dimensional time-frequency structure in the input. These techniques are applied on a small-footprint DNN-based keyword spotting task, where we find that we can reduce model size by 75% relative to the baseline, without any loss in performance. Furthermore, we find that the proposed approach is more effective at improving model performance compared to other popular dimensionality reduction techniques, when evaluated with a comparable number of parameters.
A Modularization Method for Battery Equalizers Using Multiwinding Transformers

This paper proposes a modularized global architecture using multi-winding transformers for battery cell balancing. The global balancing for a series-connected battery string is achieved based on forward conversion in each battery module and based on flyback conversion among modules. The demagnetization of the multiwinding transformers is also simultaneously achieved by the flyback conversion among modules without the need of additional demagnetizing circuits. Moreover, all MOSFET switches are driven by two complementary pulse width modulation signals without the requirement of cell voltage sensors, and energy can be automatically and simultaneously delivered from any high voltage cells to any low voltage cells. Compared with existing equalizers requiring additional balancing circuits for battery modules, the proposed modularized equalizer shares one circuit for the balancing among cells and modules. The balancing performance of the proposed equalizer is perfectly verified through experimental results, and the maximum balancing efficiency is up to 91.3%. In summary, the proposed modularized equalizer has the advantages of easier modularization, simpler control, higher efficiency, smaller size, and lower cost, ensuring the battery system higher reliability and easier implementation.
Identifying the sentiment styles of YouTube's vloggers

Vlogs provide a rich public source of data in a novel setting. This paper examined the continuous sentiment styles employed in 27,333 vlogs using a dynamic intra-textual approach to sentiment analysis. Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog’s narrative time. We provide a taxonomy of these seven continuous sentiment styles and found that vlogs whose sentiment builds up towards a positive ending are the most prevalent in our sample. Gender was associated with preferences for different continuous sentiment trajectories. This paper discusses the findings with respect to previous work and concludes with an outlook towards possible uses of the corpus, method and findings of this paper for related areas of research.
Event Specific Multimodal Pattern Mining for Knowledge Base Construction

Knowledge bases, which consist of a collection of entities, attributes, and the relations between them are widely used and important for many information retrieval tasks. Knowledge base schemas are often constructed manually using experts with specific domain knowledge for the field of interest. Once the knowledge base is generated then many tasks such as automatic content extraction and knowledge base population can be performed, which have so far been robustly studied by the Natural Language Processing community. However, the current approaches ignore visual information that could be used to build or populate these structured ontologies. Preliminary work on visual knowledge base construction only explores limited basic objects and scene relations. In this paper, we propose a novel multimodal pattern mining approach towards constructing a high-level "event" schema semi-automatically, which has the capability to extend text only methods for schema construction. We utilize a large unconstrained corpus of weakly-supervised image-caption pairs related to high-level events such as "attack" and "demonstration" to both discover visual aspects of an event, and name these visual components automatically. We compare our method with several state-of-the-art visual pattern mining approaches and demonstrate that our proposed method can achieve dramatic improvements in terms of the number of concepts discovered (33% gain), semantic consistence of visual patterns (52% gain), and correctness of pattern naming (150% gain).
Genome sequence and comparative analysis of the solvent-producing bacterium Clostridium acetobutylicum.

The genome sequence of the solvent-producing bacterium Clostridium acetobutylicum ATCC 824 has been determined by the shotgun approach. The genome consists of a 3.94-Mb chromosome and a 192-kb megaplasmid that contains the majority of genes responsible for solvent production. Comparison of C. acetobutylicum to Bacillus subtilis reveals significant local conservation of gene order, which has not been seen in comparisons of other genomes with similar, or, in some cases closer, phylogenetic proximity. This conservation allows the prediction of many previously undetected operons in both bacteria. However, the C. acetobutylicum genome also contains a significant number of predicted operons that are shared with distantly related bacteria and archaea but not with B. subtilis. Phylogenetic analysis is compatible with the dissemination of such operons by horizontal transfer. The enzymes of the solventogenesis pathway and of the cellulosome of C. acetobutylicum comprise a new set of metabolic capacities not previously represented in the collection of complete genomes. These enzymes show a complex pattern of evolutionary affinities, emphasizing the role of lateral gene exchange in the evolution of the unique metabolic profile of the bacterium. Many of the sporulation genes identified in B. subtilis are missing in C. acetobutylicum, which suggests major differences in the sporulation process. Thus, comparative analysis reveals both significant conservation of the genome organization and pronounced differences in many systems that reflect unique adaptive strategies of the two gram-positive bacteria.
An analysis of approximations for maximizing submodular set functions - I

Let N be a finite set and z be a real-valued function defined on the set of subsets of N that satisfies z(S)+z(T)>-z(SUT)+z(SnT) for all S, T in N. Such a function is called submodular. We consider the problem maXscN {z(S): IS[ <-K, z(S) submodular}. Several hard combinatorial optimization problems can be posed in this framework. For example, the problem of finding a maximum weight independent set in a matroid, when the elements of the matroid are colored and the elements of the independent set can have no more than K colors, is in this class. The uncapacitated location problem is a special case of this matroid optimization problem. We analyze greedy and local improvement heuristics and a linear programming relaxation for this problem. Our results are worst case bounds on the quality of the approximations. For example, when z(S) is nondecreasing and z(0) = 0, we.show that a "greedy" heuristic always produces a solution whose value is at least 1-[ (K-1) / K ] K times the optimal value. This bound can be achieved for each K and has a limiting value of (e-l)/e, where e is the base of the natural logarithm.
CrackIT — An image processing toolbox for crack detection and characterization

This paper presents a comprehensive set of image processing algorithms for detection and characterization of road pavement surface crack distresses, which is being made available to the research community. The toolbox, in the Matlab environment, includes algorithms to preprocess images, to detect cracks and characterize them into types, based on image processing and pattern recognition techniques, as well as modules devoted to the performance evaluation of crack detection and characterization solutions. A sample database of 84 pavement surface images taken during a traditional road survey is provided with the toolbox, since no pavement image databases are publicly available for crack detection and characterization evaluation purposes. Results achieved applying the proposed toolbox to the sample database are discussed, illustrating the potential of the available algorithms.
A Full Soft-Switching ZVZCS Flyback Converter Using an Active Auxiliary Cell

In this paper, a new soft switching flyback dc-dc converter with a simple zero-voltage and zero-current switching (ZVZCS) cell is proposed. The auxiliary resonant cell consists of one switch, one diode, one inductor, and two capacitors. It provides ZVZCS not only for the main switch but also for the auxiliary switch at both turn-on and turn-off times. In addition, all the auxiliary diodes and body diodes of the switches operate under full ZVZCS except the output diode which turns off only with zero current switching. This soft switching technique makes the proposed converter have less switching losses which leads the converter to operate in higher frequencies and efficiency. Another outstanding feature of the proposed converter is using the auxiliary resonant cell out of the main power path to have lower voltage stress on the switches. In this paper, pulse width modulation (PWM) is employed to control the main switch. A detailed mode analysis of the proposed converter operation is presented along with design procedure. Finally, the accuracy performance of the converter is verified through a 60 W experimental prototype results.
SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services

In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.
A recommender system using GA K-means clustering in an online shopping market

The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers’ needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied K-means clustering whose initial seeds are optimized by GA, which is called GA K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA K-means to those of a simple K-means algorithm and self-organizing maps (SOM). The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems.
Culture and Computer-Mediated Communication: Toward New Understandings

This collection of articles was originally inspired by several presentations at CATaC’04 and subsequent critical discussion of their use of the frameworks for cultural analyses developed by Edward T. Hall (1966, 1976) and Gert Hofstede (e.g., 1980, 1991). In response to these presentations and discussion, we developed this special thematic section for the Journal of Computer-Mediated Communication. The thematic questions that guided this collection are: